Methods and apparatuses for determining throttle settings to satisfy a system power constraint

ABSTRACT

Exemplary embodiments of methods and apparatuses to manage a power of a system that leverage intermediate power margins are described. One or more subsystems of the system are operated at one or more performance points. A power consumed by the one or more subsystems at each of the one or more performance points is measured. An operational power of the one or more subsystems at the one or more performance points is determined. The one or more subsystems are operated at well-known conditions at the one or more performance points. The operational power may be adjusted based on data associated with the one or more subsystems. The operational power is provided to a power lookup table. The power is distributed among the one or more subsystems based on the operational power.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 13/708,887, filed on Dec. 7, 2012, which is a continuation ofU.S. patent application Ser. No. 12/871,803, filed on Aug. 30, 2010,issued as U.S. Pat. No. 8,332,679, which is a continuation of U.S.patent application Ser. No. 11/327,238, filed on Jan. 5, 2006, issued asU.S. Pat. No. 7,788,516, which is a continuation-in-part of U.S. patentapplication Ser. No. 11/212,970, filed on Aug. 25, 2005, issued as U.S.Pat. No. 7,562,234.

TECHNOLOGY FIELD

At least some embodiments of the present invention relate generally todata processing systems, and more particularly but not exclusively tothe management of power usage in data processing systems.

BACKGROUND

Traditionally, computer systems are designed to be able to continuouslyrun a fairly worst-case power load. Design according to such acontinuous worst-case power load has never been much of a problem,because traditionally the individual components have had modestoperating powers and the computer systems have had large power budgetsso that the systems could sustain the load fairly naturally.

As the operating power consumptions of the individual components ofcomputer system creep upwards, the power budgets of the computer systemshave become tighter. It is now becoming a challenge to design a computersystem to run a continuous worst-case workload while pursuing other highperformance goals, such as high computing power, compactness, quietness,better battery performance, etc. For example, portable computer systems,such as laptop computers, have a limited battery output capability; andthus a worst-case workload for a given battery output capability maylimit the performance of the system because the worst case workload mayrarely occur.

Currently, substantially large additional power margins are thrown awayto ensure that the critical thresholds are not exceeded during normalsystem operation. Typically, a substantially large plurality of samplecomponents, or subsystems, is measured to produce a statistical powerdistribution curve. A worst-case power margin value for the componentsor subsystems is calculated from the statistical power distributioncurve of a vast number of sample components using, for example, a “sixsigma” method. The statistically calculated worst-case power margin is asingle fixed substantially conservative number that is valid for allcomponents or subsystems that provides a minimum guaranteed performanceand does not value the performance efficiency of the computer system.

SUMMARY OF THE DESCRIPTION

Exemplary embodiments of methods and apparatuses to manage power of acomputer system leveraging intermediate power points are described. Inone embodiment, the system includes one or more subsystems coupled toeach other. The one or more subsystems may include a microprocessor, amicrocontroller, a central processing unit (“CPU”), a graphicsprocessing unit (“GPU”), a memory, or any combination thereof. The oneor more subsystems are operated at one or more performance points. Inone embodiment, operating the subsystem at the one or more performancepoints may include operating the subsystem at a set of frequencies thatdetermine a speed of the system, operating the subsystem at apredetermined temperature, or a combination thereof. Each of thesubsystems is operated at well known conditions at each of the one ormore performance points. In one embodiment, the well-known conditionsare those that are substantially close to the thermal design point(“TDP”) for a worst-case part of the subsystem. The power consumed byeach of the subsystems at each of the performance points is measured. Anoperational power of the one or more subsystems at the one or moreperformance points is determined based on the measured power.

In one embodiment, the subsystem is operated at well known conditions ata performance point. The power consumed by the subsystem is measured.The operational power based on the measured power consumed by thesubsystem is determined. Next, the operational power may be adjustedbased on data associated with the one or more subsystems. In oneembodiment, the data associated with the subsystem may be a feedbackdata associated with the performance of the subsystem. In oneembodiment, data associated with the subsystem may include a signalassociated with the temperature of a component incorporated into thesubsystem, e.g., an assertion of PROCHOT# (hereinafter PROCHOT-L) pin,as it is performed on computer chips produced by Intel Corporation,located in Santa Clara, Calif. In one embodiment, the operational powermay be adjusted to add extra power margins. The extra power margins maybe added to include a measuring error, measuring accuracy, and/ormeasuring resolution. Next, the operational power is provided to a powerlookup table stored in a memory.

In another embodiment, one or more subsystems of a computer system isoperated at well known conditions, e.g., to provide a maximum powerconsumption by each of the one or more subsystems, at one or moreperformance points. The power consumed by each of the one or moresubsystems at each of the one or more performance points is measured. Anoperational power of each of the one or more subsystems is determinedbased on a measured power. Next, the operational powers are provided toone or more power distribution tables. Next, the power is distributedamong the one or more subsystems based on the operational powers.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 illustrates a method to dynamically control power usage accordingto one embodiment of the present invention.

FIG. 2 illustrates a method to dynamically determine power usage budgetaccording to one embodiment of the present invention.

FIG. 3 illustrates a method to dynamically distribute power usage budgetaccording to one embodiment of the present invention.

FIG. 4 illustrates a block diagram example of a system to dynamicallycontrol power usage according to one embodiment of the presentinvention.

FIG. 5 illustrates an example of dynamically throttling components of adata processing system to control power usage according to oneembodiment of the present invention.

FIG. 6 illustrates an example of using throttle settings of a centralprocessing unit (CPU) of a data processing system to control power usageaccording to one embodiment of the present invention.

FIG. 7 illustrates an example of using throttle settings of multiplecomponents of a data processing system to control power usage accordingto one embodiment of the present invention.

FIG. 8 shows a method to dynamically determine throttle settingaccording to one embodiment of the present invention.

FIGS. 9-10 illustrate scenarios of power usage according to embodimentsof the present invention.

FIG. 11 illustrates a table to look up the power usage requirement ofthe non-throttled component based on signal states according to oneembodiment of the present invention.

FIG. 12 illustrates a computer system with a power management systemaccording to one embodiment of the present invention.

FIGS. 13-16 illustrate methods of power management according toembodiments of the present invention.

FIG. 17 shows a block diagram example of a data processing system whichmay be used with the present invention.

FIG. 18 is a flowchart of one embodiment of a method to dynamicallyredistribute power in a system.

FIG. 19 is a flowchart of one embodiment of a method to dynamicallyredistribute power based on a load profile of a system.

FIG. 20 is a flowchart of another embodiment of a method to dynamicallyredistribute power based on a load profile of a system.

FIGS. 21A-21C illustrate one embodiment of power distribution tablesbuilt for a system that includes a CPU and a GPU subsystems.

FIG. 22 illustrates one embodiment of one of the power distributiontables associated with a load profile Kn for a system, which includes aplurality of subsystems 1 to N.

FIG. 23 is a flowchart of one embodiment of a method to dynamicallyredistribute power while tracking a load profile of a system whenanother subsystem is added to the system.

FIG. 24 illustrates one embodiment of a system to dynamicallyredistribute the power while tracking a load profile of a system.

FIG. 25 is a flowchart of one embodiment of a method to adjust a targettemperature of a computer system.

FIG. 26A illustrates one embodiment of a system having a component, suchas one or more microprocessors, coupled to a heat sink.

FIG. 26B shows a model of heat dissipation from a component through aheal sink.

FIG. 27 is a flowchart of one embodiment of a method of operating anadaptive cooling control system of a computer system.

FIG. 28 is a flowchart of one embodiment of a method to adjust a targettemperature of a heat sink based on a component-specific signal.

FIGS. 29A-29C illustrate alternate embodiments of signals associatedwith the temperature of the component.

FIG. 30 is a flowchart of another embodiment of a method of using acomponent-specific signal in a cooling system that includes a heat sink.

FIG. 31 is a flowchart of one embodiment of a method to operate acooling system that includes a heat sink.

FIG. 32 illustrates one embodiment of a computer system having anadaptive cooling arrangement.

FIG. 33 is a flowchart of one embodiment of a method to manage the powerof a computer system that leverages intermediate power points.

FIG. 34 is a flowchart of one embodiment of a method of providing anintermediate processor power point for a subsystem.

FIG. 35 is a flowchart of another embodiment of a method of usingintermediate operational power points to distribute power in a computersystem.

FIG. 36 is a flowchart of one embodiment of a method of determiningintermediate operational power points of one or more subsystems of acomputer system.

FIG. 37 illustrates one embodiment of a system that leveragesintermediate operational power points.

FIG. 38 illustrates one embodiment of an intermediate operational powerfor a subsystem at various performance points.

DETAILED DESCRIPTION

The following description and drawings are illustrative of the inventionand are not to be construed as limiting the invention. Numerous specificdetails are described to provide a thorough understanding of the presentinvention. However, in certain instances, well known or conventionaldetails are not described in order to avoid obscuring the description ofthe present invention. References to one or an embodiment in the presentdisclosure are not necessarily references to the same embodiment; and,such references mean at least one.

In one embodiment of the present invention, it is recognized that powerconsumptions in a computer system typically change frequently duringtypical usages. Typically, not all components are simultaneously in themaximum power consumption mode. Further, some components may not be inthe maximum power consumption mode continuously for a long period oftime. The power consumption of a component, such as the centralprocessing unit (CPU) microprocessor, changes dramatically over time intypical usages. For example, in the prior art, the power supplies or abattery pack of computer system were designed to produce enough power tosatisfy a worst case scenario in which all of the components of thecomputer system were drawing their maximum (in this case, worst level)amount of power. This worst case scenario essentially adds together theworst case, maximum level, power consumption; and the design takes thisscenario into account when selecting a power supply or a battery pack.Thus, designing a computer system to sustain the worst-case load can beoverly conservative for typical usages.

In one embodiment of the present invention, a computer system isdesigned to optimize various design goals for typical usages. However,worst-case load cases can occur. To avoid damage to the computer system,a dynamic power management system is used to dynamically budget thepower usage of at least some of the components of the computer systemsuch that, when the heavy tasks are imposed on the system, the systemcan trade performance for power consumption to stay within the powerusage limit.

FIG. 1 illustrates a method to dynamically control power usage accordingto one embodiment of the present invention.

In FIG. 1, a computer system has a number of different throttlesettings. For example, the CPU processor of the computer system may beset to run under different core voltages and/or different corefrequencies such that the system can be fully functional but atdifferent performance levels to trade power usage and computingperformance.

Typically, different throttle settings correspond to differentperformance levels. Further, different settings have different powerrequirements (e.g., 121, 123, 125, . . . , 129).

The power requirement at each setting is the maximum possible powerusage at the corresponding setting. However, depending on the tasksbeing performed, the actual power consumptions can vary within a range.

For example, at setting X, the power usage range (129) is between theminimum possible power consumption (109) (e.g., when the system is inidle) and the maximum power consumption (107) (e.g., when the system isfully busy).

In one embodiment of the present invention, the system is so designedthat the limit (103) for time averaged power usage is less than thepower requirement (107) for setting X. Thus, if the system were allowedto be fully busy for a long period of time, the system might be damaged.However, it is recognized that in a typical usage the average of thedynamic power usage range (129) may be lower than the limit (103) sothat the system can be in the setting X for a long period of time forhigh performance.

In one embodiment of the present invention, when the system receives atask that requires the system to be fully busy for a long period oftime, the system automatically switches to a different setting (e.g.,setting C) such that the limit (103) is not violated.

One embodiment of the present invention dynamically budgets the powerusage for components of a data processing system, which may have a powerusage constraint, such as thin-and-light portables computers, or largerportables, and/or small consumer desktops, for example, the constraintof heat dissipation on a computer system with a compact housing maylimit the power usage. For example, to maintain the performance of abattery pack, there is a limit on average battery discharge current.Although the battery may allow a much higher instantaneous dischargecurrent, the battery may have a much lower limit on average dischargecurrent for a period of time to prevent significantly degrading theperformance of the battery, or causing irreversible damage to thebattery.

In one embodiment of the present invention, computer systems (e.g.,portable computers or desktop computers) are designed for typical usagesand may not sustain a continuous worst-case power load for a long periodof time. Typically, a burst of computer tasks may require aninstantaneous peak power load, which lasts only for a short period oftime before the tasks are completed. Before and after the burst of thetasks, the computer system typically consumes a power load that is muchlower than the worst-case power load. Since certain power usage limitsare not based on the instantaneous power usage (e.g., the power limitsbased on thermal/heat dissipation constraint, based sustainable batterydischarge current, etc.), such a temporary burst of tasks may betolerable to allow high performance under tight power budget fortypically usages.

Thus, with at least certain embodiments of the invention, performancelevels (and power consumption levels) are set, for a short period oftime (e.g., burst activity periods), to exceed a continuous state powercapacity of the system (e.g., a power supply, or a battery pack).Traditionally, a computer system is designed according to the continuousworst-case workload; thus, no power usage range is allowed to exceed alimit for maximum continuous usage of a power supply (e.g., the capacityof a power supply to sustain a continuous state of constant powerusage). For example, in a traditional design, no power usage range wouldexceed the limit (103), since the worst-case workload is traditionallytreated as a continuous constant load. In one embodiment of the presentinvention, one or more power usage ranges (e.g., 129) is allowed toexceed the limit (103) for a limited continuous period of time. In oneembodiment of the present invention, the power usage of the system ismanaged such that the actual power usage is in average (e.g., over atime window based on a time constant of the power suppler) below thelimit (103).

Although the power stack-ups which happen under worst-case power loadcan happen, they rarely do happen. Far more often one encounters asituation where some parts of the computer system are operating at fullpower, and other parts of the system are operating at low power. Forexample, when one is performing a scientific computation, the processorand the memory are very busy and are consuming close to full power, butthe optical disk drive is empty and is consuming close to no power. Whenone is playing a DVD, the optical disk drive is consuming close to fullpower because it is reading the DVD, and the display is also consumingclose to full power because it is very bright, but the processor andmemory are consuming only modest power because decoding a DVD is not avery demanding application.

In one embodiment of the present invention, the power of a computersystem is redistributed and throttled dynamically to ensure that theactual power usage is within the power usage constraints.

In one embodiment of the present invention, dynamic power redistributionis used to design a computer system for a typical power load over someaveraging interval, as opposed to designing it for a continuousworst-case power load. Because a typical power load, in an averagesense, is less than a worst-case power load, the system designed in thisway can be constructed from higher-power components (which may becheaper, or may have higher performance), or can be made physicallysmaller.

Because worst-case power stack-ups rarely do happen, a system designedin this fashion performs as well as a system designed in the traditionalway in all but the most pathological situations. Unfortunately,worst-case power stack-ups can happen. Since a system designed fortypical power cannot naturally handle worst-case conditions, oneembodiment of the present invention uses throttling to ensure that thepower constraints are not violated.

For example, in FIG. 1, the system dynamically determine the “best”allowable setting according to the actual past power usage informationto ensure that even if the computer is fully busy in the next timeinterval at the selected setting, the limit (103) will not be violated.

In one embodiment of the present invention, the system implementscontrols (throttles) on a subset of its subsystems which limit themaximum power that could be consumed by those subsystems. Adjusting thethrottle settings can adjust the maximum power a subsystem (component)can use. Typically, these throttles limit the performance of thesubsystem. For example, different throttle settings may be designed fordifferent performance levels of the subsystem; and the power usage islimited as a side effect.

For example, the CPU (central processing unit) microprocessor may workusing different core voltages and core frequencies. Using a high corevoltage and a high frequency, the microprocessor can be optimized forcomputational performance but it has high power consumption. Using a lowcore voltage and a low frequency, the microprocessor can be optimizedfor battery performance at a degraded computational performance level.

In one embodiment, the microprocessor can shift from one core voltagelevel to another and from one frequency to another through slowlychanging the voltage and through slowly change the frequency, withoutstopping or pausing the computational tasks of the processor. Typically,the voltage and the frequency are changed separately. For example, tomove from a high frequency and a high core voltage to a low frequencyand a low core voltage, the system changes the frequency while at thehigh voltage and then changes the voltage while at the low frequency.For example, to move from a low frequency and a low core voltage to ahigh frequency and a high core voltage, the system changes the voltagewhile at the low frequency and then changes the frequency while at thehigh voltage. Further details on changing CPU core voltage and frequencycan be found in U.S. patent application Ser. No. 10/917,719, filed Aug.12, 2004, which is hereby incorporated herein by reference.

In one embodiment of the present invention, the system dynamicallyredistributes power and sets these throttles so that even when theworst-case power load for these throttles occurs, the maximum powerconsumed over the averaging interval does not exceed the limit. Sinceworst-case power loads are rare, the throttle controls can normally beset to very high values, such that the system acts as if the limit doesnot exist when the demand of the system is lower than the dynamicallydetermined budget.

FIG. 2 illustrates a method to dynamically determine power usage budgetaccording to one embodiment of the present invention.

In FIG. 2, actual power usages are monitored. For example, the actualpower usage can be measured periodically to determine the history of thepower usage. The history of the power usage can be used to determine thepower usage in certain averaged ways.

In one embodiment of the present invention, with the knowledge of thepast power usage (e.g., measurements 211, 213, . . . , 219 at timesT−(n−1)Δ, T−(n−2)Δ, . . . , T) the system can dynamically determine theallowable power budget for the next time interval (e.g., for time T+Δ).

For example, the limit (203) may be a simple average in a time window(221) of a time period (n+1)Δ (e.g., less than 10 seconds). Thus, in theexample of FIG. 2, the actual past power usage (e.g., 211, 213, . . . ,219) can be used to determine the power budget (205) such that the area(207) of the power budget that is above the limit (203) is equal to orless than the area (209) between the limit (203) for average power usage(203) and the curve of the past power usage.

The system is then throttled to a setting that will not require powerusage more than the dynamically determined budget (205).

In the next time period (e.g., T+Δ), the actual power usage is measured,which is typically smaller than the power budget (205). Using the newlymeasured power usage information and the time window that advances withtime for computing the average, the power budget and throttle settingfor a further time period can be determined in a similar way.

In one embodiment, the power budget (205) is further limited accordingto other conditions, such as the limit (201) for instantaneous powerusage.

Further, there may be a number of different types of average-based powerusages (e.g., with different weight in averaging, based on differentpower limitation considerations, etc.) Thus, multiple copies of thepower budget can be determined from a set of different computations,based on past power usage information; and the lowest power budget canbe selected as the limiting budget that determines the throttle setting.

In one embodiment, the measurement is an instantaneous data sample; andthe frequency of the data samples are such that the changing history ofthe power usage is captured in a reasonably accurate way. Alternatively,the measurements may be obtained through an integration process suchthat even a small number of data samples can represent the power usagehistory with sufficient accuracy.

Note that the data samples may or may not be collected at the same timeinterval as that for computing the power budget and throttle setting. Inone embodiment, the time period for determining the throttle setting issufficiently short in comparison with the window (221) to allowflexibility in budgeting and long enough to provide sufficient time forthe system to transit from one throttle setting to another whennecessary and work reliably in the selected throttle setting.

The time period for determining the throttle setting may or may not beconstant. For example, when a significant power usage event occurs(e.g., when the requirement estimate of the non-throttled componentschanges), the system may automatically start a new computation of thethrottle setting.

FIG. 3 illustrates a method to dynamically distribute power usage budgetaccording to one embodiment of the present invention.

In FIG. 3, the dynamically determined power budget (301) is to beallocated to different components (subsystems) of the system.

In one embodiment, the system includes throttled component(s) andnon-throttled component(s). A throttled component has different throttlesettings at which the component is functional but at differentpower/performance levels (operating setting). For example, a processormay be throttled to work at different core voltages and corefrequencies; a disk drive may be throttled to work at different spinrate; a bus may be throttled at different frequencies; etc. If acomponent is not throttled to trade performance for power usage, thecomponent is considered a non-throttled component.

In FIG. 3, the power budget (301) includes a throttled portion (303) anda non-throttled portion (305). The non-throttled portion corresponds tothe estimated power used by non-throttled component(s) (307). Thethrottled portion is determined from the difference between thedynamically determined power budget (301) and the estimated power usedby the non-throttled component(s).

In general, there can be one or more throttled components. When thereare multiple throttled components, the throttle settings determined forthe throttled components are such that the sum of the maximum powers(e.g., 311, 313, . . . , 319) that can be used by the correspondingthrottled components is no more than the throttled portion of thebudget. Thus, the maximum powers (e.g., 311, 313, . . . , 319) that canbe used by the corresponding throttled components can be considered asbudgets for the throttled components; and the throttle settings ensurethat the actual powers (e.g., 321, 323, . . . , 329) used by thethrottled components are no more than their dynamically determinedbudgets (e.g., 311, 313, . . . , 319).

Typically, the components (subsystems) whose throttle is adjusted maynot actually consume the entire amount of power that is budgeted, sincethese components may not be busy enough to run at the maximum powercorresponding to the throttle setting. The adjustment of the throttleallows the subsystem the freedom to consume up to the worse case powerload for the corresponding throttle setting without violating powerconstraints.

When a worst-case power load appears, the system quickly notices theneed for dynamic power redistribution and sets the throttles to lowervalues, keeping the system within its operating limits. In general, thepower redistribution may be in the form of redistributing amongsubsystems (components) and/or redistributing over the time for the samesubsystem (component) among different performance levels.

Imagine a system with an empty DVD drive that is running a scientificapplication. The processor and memory subsystems of the system areconsuming close to full power, making the whole system run close to itsoperating limits. Now imagine that a disk is loaded into the DVD drive,which means that the DVD drive is to be enabled, consuming considerablepower. In order to actually have power to enable the DVD drive, thesystem adjusts its power budget so that some of the power which used tobe allocated to the processor is now allocated to the DVD drive; thethrottle associated with the processor is switched to a lower value.

In one embodiment of the present invention, the averaging interval is(relatively) long with respect to the rate at which the dynamic powerredistribution is executed. The allows the system to notice that thesystem is close to exceeding its limits, and have time to adjust thethrottles and ensure that the system does not actually exceed itslimits. The typical parts of the system which have power limits(batteries, heat sinks) tend to have fairly long time constants. Thus,it is easy to select a rate at which the dynamic power redistribution isexecuted.

Although power is referred to as an example of embodiments of thepresentation inventions, other parameters related to power can also bemanaged in a similar way. For example, battery discharge current can bemanaged in a similar way as power.

In one embodiment of the present invention, a system with dynamic powermanagement according to embodiments of the present invention includesone or more components (subsystems) that can be throttled to havedifferent power requirements at different performance levels and haveone or more sensors to actually determine the power consumed.

In one embodiment of the present invention, the past history of actualpower consumption is used to dynamically determine the power usagebudget for the subsequent time interval, such that even if theworst-case load occurs in the subsequent time interval the power usageconstraint (e.g., average power usage, or average battery dischargecurrent) is not violated.

In one embodiment of the present invention, the actual power consumed byeach subsystem is determined for the dynamic power redistribution andthrottling.

In one embodiment of the present invention, instead of determining theactual power consumption by each subsystem, the sum of the powerconsumed by the throttled subsystems and the sum of the power consumedby the non-throttled subsystems are determined and used for thethrottling.

FIG. 4 illustrates a block diagram example of a system to dynamicallycontrol power usage according to one embodiment of the presentinvention.

In FIG. 4, a microcontroller (411) is used to budget the power usagedynamically. The power supply (401) (e.g., battery, AC adapter, etc.)provides power to the throttled component(s) (409) (e.g., CPU) and thenon-throttled component(s) (405) (e.g., hard drive, DVD ROM, etc.). Themicrocontroller (411) can be considered as part of the non-throttledcomponents. Alternatively, the microcontroller (411) may draw power froma power supply different from the power supply (401). Sensors (407 and403) are used to determine the actual power usages by the throttledcomponent(s) (409) and the non-throttled component(s). Themicrocontroller (411) collects the actual power usage information fromsensors (407 and 403) and communicates with throttled components (409)to make throttle changes.

In one embodiment, a single sensor or measuring device may be used tomeasure power drawn by several non-throttled devices (rather than havingone sensor for each non-throttled device). For example, wires may bearranged to connect to several non-throttled devices; and the measuredpower is that consumed by all of the non-throttled devices connected. Asensor can be used to determine the sum of the power consumed by thesystem directly (e.g., using a single current sensor at a location wherethe current drawn by the throttled components and the current drawn bythe non-throttled components merges) and to determine the dynamicthrottle setting.

Alternatively, this approach may be implemented by, for example,coupling the single sensor to wires from each of the severalnon-throttled devices, and the measured currents and/or voltages aresummed in the sensor. Alternatively, multiple sensors can be used; andthe microcontroller (or the microprocessor) sums the measurements fromthe sensors.

For example, the microcontroller may store the determined throttlesetting in a register and then send a signal to the correspondingcomponent (or the main CPU) to enforce the throttle setting. In oneembodiment, the microcontroller sends the signal to enforce the throttlechange only when the dynamically determined throttle setting isdifferent from the previous one.

In one embodiment, the sensors are implemented using hardware.Alternatively, at least some of the sensors can be implemented usingsoftware. For example, software modules may be used to determine theoperation states and corresponding time periods to compute the actualpower usage from predetermined power consumption rate for the operationstates.

FIG. 5 illustrates an example of dynamically throttling components of adata processing system to control power usage according to oneembodiment of the present invention.

In FIG. 5, the actual power usages include the power used by thethrottled components (e.g., 521, 511, 513, . . . 517) and the power usedby the non-throttled components (e.g., 523, 512, 514, . . . 518).

In one embodiment, the power used by the non-throttled components at thesubsequent time interval (e.g., T+Δ) is determined using the worst-casepower load of the non-throttled component. Alternatively, thenon-throttled components may be interrogated to obtain the worst-casepower load of the non-throttled component according to their currentoperating status.

Alternatively, operating signals of at least some of the non-throttledcomponents can be used to classify the corresponding non-throttledcomponents into a global operating state, which is used to obtain anestimate that corresponds to the global operating state.

In one embodiment, the raw sensor measurements are used directly by amicrocontroller or a microprocessor to perform dynamic power budgeting.Alternatively, the raw sensor measurements may be further processedusing hardwire (e.g., using analog or digital circuitry) to generatedata that is used by the microcontroller or microprocessor to performdynamic power budgeting. Thus, there may be a layer of algebra betweenthe raw sensors and the throttled and non-throttled powers.

In one embodiment, dynamic power redistribution is performed frequentlyand periodically. For example, the dynamically determined throttlesetting can be determined periodically at a predetermined time interval.However, it is understood that the time interval is not necessarily aconstant. For simplicity, some examples with a constant time intervalare used to illustrate the methods according to embodiments of thepresent invention.

In one embodiment of the present invention, the setting for a throttleis computed for the next time interval to guarantee that the averagepower over the last N time intervals, including the next time interval,is less than a power constraint PMAX. Thus, when the measured power datafrom the last N−1 time intervals is combined with the hypotheticalworst-case power data of 1 sample for the next time interval, theaverage power is no more than PMAX.

In general, the averaging process of the power usage over the last Ntime intervals can be a simple average, or a weighted average (e.g.,weighted according to the elapsed time with decreasing weight for awayback into the past), or other complex functions of the power usagehistory.

For example, let PT[N−1:1] be an array containing the measured powerdata of the throttled parts of the system for the last N−1 timeinterval. Let PN[N−1:1] be an array containing the measured power dataof the non-throttled parts of the system. To determine the throttle forthe next time interval:

1) update the array containing the measured power data of the throttledparts of the system. For example, PT[N−2:1] can be copied (e.g., throughshifting) into PT[N−1:2]; and a new measurement of the sum of the powerdata of the throttled part of the system is stored into PT[1]. Theoldest sample, which was in PT[N−1], is discarded.

2) similarly, update the array containing the measured power data of thenon-throttled parts of the system. For example, PN[N−2:1] can be copied(e.g., through shifting) into PN[N−1:2]; and a new measurement of thesum of the power data of the non-throttled part of the system is storedinto PN[1]. The oldest sample, which was in PN[N−1], is discarded.

3) compute EPN, which is an estimate of average value of thenon-throttled power over the last N samples, from the average of the N−1power measurements in PN[N−1:1] and one estimate of the maximum powerPN[0] which may be consumed by the non-throttled parts of the system.

4) for ith throttle setting, computer ETN[i], which is an estimate ofthe average value of the throttled power over the last N samples, fromthe average of the N−1 power measurements in PT[N−1:1] and the estimateof the maximum power PT[0, i] which may be consumed by the throttledparts of the system at throttle setting i.

5) determine the highest throttle setting im for which EPN□ETN[im] isless than or equal to PMAX. Thus, when throttle setting im is used, theaverage power through the next time interval will be less than or equalto the maximum power allowed by the system.

Note that in general, any throttle setting ix for which EPN□ETN[ix] isless than or equal to PMAX can be selected without exceeding the limitPMAX. In one embodiment, the throttle settings are arranged according toperformance level. The higher the throttle setting, the higher theperformance. Thus, the highest throttle setting that limit the powerusage according to PMAX is selected to allow the highest performanceunder the power constraint.

FIG. 6 illustrates an example of using throttle settings of a centralprocessing unit (CPU) of a data processing system to control power usageaccording to one embodiment of the present invention.

In FIG. 6, a number of different combinations of CPU core voltages andcore frequencies are sorted so that the throttle setting increases withthe performance level, as illustrated in table 601. In one embodiment,the system searches in the order of decreasing throttle setting todetermine the first throttle setting that satisfies the relationEPN□ETN[ix]≦PMAX.

Thus, when a throttle setting is determined, both the CPU core voltagesand frequencies are determined.

Alternatively, the throttles may be sorted according to other goals(e.g., a combined goal indicator to reflect the requirement for highcomputing power and low energy consumption, etc.); and a “best” throttlesetting can be searched in a similar way.

Note that if there are multiple independent throttles, a list ofdifferent combination of throttles can be examined to determine theallowable throttle settings. A “best” setting of the throttles can beselected according to certain rules that define the objective “best”. Itis understood that the rules for define the objective can be arbitrarilycomplex.

FIG. 7 illustrates an example of using throttle settings of multiplecomponents of a data processing system to control power usage accordingto one embodiment of the present invention.

In FIG. 7, multiple components have independent throttle settings, asillustrated in table 701. To distribute the dynamically determinedbudget to the multiple components, different combinations of thethrottle settings for the multiple components can be viewed as differentglobal throttle settings. The global throttle settings can be sortedaccording to a target goal level.

In one embodiment, the sorting of the global settings can be performedat the design stage of the computer according to a static fixed targetgoal function, or manually arranged by the designer of the system.

Alternatively, the global settings can be performed in real timeaccording to a target goal function, which may be a function of currentstate of the computer system. For example, some of the components may bebusy so that require higher priority while others may be in idle andrequire lower priority. Thus, the target function can be constructed toinclude the consideration of the current workloads of the components.The workloads can be estimated from the history of the actual powerconsumptions. For example, the high power consumption with respect tothe dynamic power range of the component indicates a high workload forthe component.

Once the global settings are sorted according to the target goal level,the highest global setting that satisfies the power constraint isselected.

FIG. 8 shows a method to dynamically determine throttle settingaccording to one embodiment of the present invention.

In FIG. 8, the throttle settings are sorted according to the powerrequirements. Since the power requirements are typically known at thedesign stage, the sorting can be performed once during the design of thesystem.

The dynamically determined power usage limit (301) is partitioned intothe non-throttled portion (305) and the throttled portion (303). Thenon-throttled portion (305) corresponds to the estimated power (307)used by the non-throttled components in the subsequent time interval.

The power budget (811) for the throttled components can then be used todetermine the set of throttle settings (813) that are within the powerbudget limit (e.g., 801, 803, . . . ,805). The throttle settings thatare outside the power budget limit (815) will be excluded fromconsideration for the next time interval (e.g., 807).

The system then can select one from the allowable set of throttlesettings (813) to optimize a performance goal.

In one embodiment, when the previous actual power usage is low, thepower budget (811) for the throttled component(s) can be sufficientenough to allow all throttle settings.

Typically, a selected throttle setting is used until the powermeasurement for the next time interval is obtained and the nextiteration of dynamic throttling is performed.

Alternatively, the throttle setting may be determined on a substantiallycontinuous basis; and the power management system requests throttlesetting changes when necessary. To avoid frequent changes in throttlesettings, the power management system may determine the throttle settingso that the throttle setting will be valid for at least a predeterminedperiod of time unless a significant change in the estimate of the powerused by the non-throttled components is detected (e.g., when a disk isloaded into the DVD ROM drive).

In one embodiment of the present invention, the power managementmonitors the actual power usage and adjusts the throttling to avoid theviolation of power constraints.

FIGS. 9-10 illustrate scenarios of power usage according to embodimentsof the present invention.

In the scenario of FIG. 9, the computer system processes (903) lowdemand tasks before time instance (911); and the actual power (921) usedby the system is below the limit (915) for average power usage. Sincethe power usage of the system is low, the system can be at the topperformance setting (901).

After time instance (911), a high demand task is received. The systembecomes busy in processing (905) the high demand task. Since the systemhad low power consumption before the reception of the high demand task,the power management allows the system to remain in the top performancesetting for a short period of time. Thus, the actual power usage (917)increases to above the limit for average power usage. However, inaverage, the actual power usage is still below the limit.

After the high demand task is finished at time instance (913), theactual power usage (923) comes back to below the limit (915) for averagepower usage. Thus, the system can remain (901) in the top performancesetting to process (907) low demand tasks.

The usage pattern as illustrated in FIG. 9 can be a typical one forcertain usages of the system. Thus, the system can be designed on atight power constraint while capable of running at top performancesetting as if it were designed according to a worst-case load whichwould require a much higher power capacity.

However, the high demand task can be such that it may take a long periodof time to finish the task. If the system were allowed to be in the topperformance setting for a long period of time, the limit (915) foraverage power usage would be violated. A system according to embodimentof the present invention can automatically detect such situations andthrottles accordingly to avoid exceeding the limit.

For example, in FIG. 10, before time instance (1021) of receiving a highdemand task, the system processes (1011) low demand tasks, as indicatedby the low actual power usage (1037).

After the time instance (1021), the system processes (1013) the highdemand task for a period of time.

Initially, the system remains in the top performance setting, whichcauses the actual power usage to be above the limit (1041) for averagepower usage. At the time instance (1023), the power managementrecognizes that the average of the actual power usage in the past periodis approaching the limit (1041); and the system throttles into a reducedperformance setting (1003).

At the reduced performance setting, the actual power consumption (1033)is below the limit (1041) for average power usage. Thus, at timeinstance (1025), the average power usage in the past may fall below thelimit (1041) enough to allow the system to temporary back to the topperformance setting (1005).

When the processing of the high demand task lasts for a long period oftime, the system automatically switches between the top performancesetting and the reduced performance setting periodically to have a longterm average that is close to the limit (1041) for the average powerusage.

Thus, under the control of the dynamic throttling system, the systemprocesses the high demand task as fast as possible within the limit ofpower constraint.

In one embodiment of the present invention, multiple copies of throttlesettings can be determined based on different constraints, for example,one for each boundary condition of power. The lowest one of the multiplecopies of throttle settings is then used to ensure that the allconstraints are satisfied. Typically, the performance is set by thesubsystem which is most constrained.

The estimate of the maximum power which may be consumed by thenon-throttled subsystems can be computed by a simple worst-case analysis(adding together the maximum values which could happen under anyconditions) or by a more elaborate analysis based on the informationprovided by the subsystems and detailed knowledge of the subsystem'sstate.

The dynamic throttle setting determination can be performed in a varietyof components in the computer system, including the main processor ofthe computer system, or a microcontroller dedicated to the dynamic powerthrottling task.

There are advantages to execute the dynamic budgeting in the mainprocessor, such as reduced cost, and the elimination of any need tocommunicate between the main processor and whatever other agent that isalternatively used to perform the task. However, it is difficult to makean arrangement such that dynamic power management operates in allsituations, including when the software in the main processor fails oris replaced with some other software which has no knowledge of the powermanagement algorithm. Further, when the computer system is in anotherwise idling state, the periodic power management task may preventthe system from entering a low power state, or may periodically wake thesystem from the low power state.

When the ability to load throttle settings is reserved to the mainprocessor of the computer system and the dynamic power throttledetermination is not performed in the main processor, making thecomputed throttle setting the current throttle setting may becomecomplicated. The throttle settings need to be communicated to the mainprocessor; and in some situations, it may be necessary to implementfail-safe mechanisms to deal with the (unlikely) case that the softwarerunning in the main processor ignores the command to load the throttles.The fail-safe mechanisms can be fairly crude, since they should only beengaged in emergency situations. For example, when the microcontrollerdetermines that the average of the past N samples exceeds the powerlimit PMAX for a number of continuous time intervals, themicrocontroller may assume that the throttling settings are not enforcedproperly and automatically initiate a shutdown process.

In one embodiment, the estimation of the power usage is obtained fromadding together the maximum powers which could be consumed by thenon-throttled subsystems (components). Such an estimate can be done whenthe system is designed; and the result can be a constant. However, suchan estimate is extraordinarily conservative, which may unnecessarilycause the system to force a throttled subsystem (component) into a lowperformance setting.

In one embodiment, the main processor performs a fairly detailed poweranalysis based on the characteristics of the subsystems and the currentstate of the subsystems. The analysis result is then used to determinethe maximum power the non-throttled subsystems can consume at thecurrent state of the operating conditions of the subsystems.

For example, the main processor may look at all of the system's USB(Universal Serial Bus) ports, and, if a device is actually plugged intothe port, extract the descriptor from the device which reports thedevice's power consumption, and use the information from the descriptorin the power analysis.

Such a detailed analysis can result in best possible estimate. However,such a detailed analysis may require non-trivial changes to softwarerunning on the main processor to provide the power consumptioninformation.

In one embodiment of the present invention, the signals used for normaloperation of a subsystem (component) are used to determine the globalstate of the subsystem (component). The power requirement for thecurrent global state is then used to determine the power requirement ofthe subsystem. Such an approach can generally improve the estimation ofthe power requirement of non-throttled components of the system withouttaking on the complexity of a detailed analysis, or making non-trivialchanges to the software.

In one embodiment of the present invention, it is observed that many, ifnot most, of the non-throttled subsystems operate in one or more globalstates, and those states can be distinguished by looking at signalsalready necessary for the operation of the subsystem.

For example, a USB port is either in the empty state (where it consumesno power) or the in-use state (where is can consume as much as 2.5 W ofpower). These states are easily distinguished by looking at the enablesignal on the USB power switch.

A USB port has a power switch which is enabled by software when a deviceis plugged in, and disabled by software when the device is unplugged.The power management can look at the digital enable for the power switchto learn if the connector is empty or full, which lets it decide if itshould use 0.0 W or 2.5 W in the calculation.

Alternatively, a crude power measurement for the USB port can be used todetermine whether or not the USB port is in the 0.0 W mode. Such a powermeasurement approached can be used in a system which does notenable/disable the switches.

Ports for an IEEE-1394 serial bus is like USB, except that the deviceconnected to the IEEE-1394 port can draw more power than a USB device.In one embodiment, when the IEEE-1394 port is on, the power managementbudgets 8 W for the device.

A crude measurement of the disk drive current can tell if the disk isspinning or not (it can tell sleep state from idle state). If the diskis spinning, the maximum power that can be consumed by the disk driveis, for example, 2.5 W (e.g., for read/write access). If the disk is notspinning, it might be told to spin up, so the maximum power that can beconsumed power is, for example, 5.0 W for a brief instant (e.g., forspin-up).

For example, a display backlight operates at a number of brightnesslevels, each with different power consumption; and these brightnesslevels are easily distinguished by looking at the PWM (Pulse-WidthModulation) control signal running between the display controller andthe backlight power supply. A measurement of the duty factor of the PWMsignal which runs between the graphics chip and the backlight invertercan be used to estimate the power consumed by the backlight. Forexample, a very dim backlight is less than a watt; and a full brightnessbacklight is close to 6 watts. Thus, improvement on the estimation ofpower used by the display backlight can significantly improve theestimation of power consumption by the non-throttled components.

For example, the PRSNT1#/PRSNT2# pin signals on the PCI (PeripheralComponent Interconnect) slot (or similar pin signals from otherinterconnect slots, such as a PCI Express slot) could be used toestimate the power which might be consumed by the device plugged intothe slot and to determine if a slot is empty.

In one embodiment, improved non-throttled power estimation is obtainedfrom bringing the signals which allow the global power states to bedistinguished to the agent (e.g., the microcontroller or the mainprocessor) which actually needs the estimate. The power of any subsystemfor which no state signal is available to determine the global states isassumed to need maximum power that could be consumed by the subsystem,but the power for any subsystem for which a state signal is available isassumed to be the maximum power in its current state.

Thus, no additional software modules are required to be running on themain processor for the purpose of reporting power requirements otherthan the software needed to actually run the device. Although theestimate it computes is no better than a worst case estimate insituations where all of the non-throttled subsystems are busy, itprovides a considerably better than worst-case estimate in situationswhere some of the non-throttled subsystems are running at less thanworst case. The inventors know that this is a very common case in normaloperation. Thus, the approach of using the existing operating signals inthe estimation can provide a better estimate for typically usages.

FIG. 11 illustrates a table (1101) to look up the power usagerequirement of the non-throttled component based on signal statesaccording to one embodiment of the present invention. In one embodiment,the states of the signals are based on the existing signals are designedfor the normal operations of the device. Thus, no special design orsoftware module is necessary to obtain the power usage information fromthe device.

FIG. 12 illustrates a computer system with a power management systemaccording to one embodiment of the present invention.

In FIG. 12, interconnect (1203) connects various components (e.g., 1211,1213, . . . , 1219, 1221, . . . , 1229) with the main microprocessor(s)(1201).

In FIG. 12, the power manager (1207) (e.g., implemented as amicrocontroller) is used to dynamically determine the throttle settingsof the system to balance the performance requirement and the power usagelimit.

Signal sensors (1205) are used to monitor selected operating signalsfrom some of the components (e.g., 1211, 1213, . . . , 1219). Theseoperating signals are used by the components (1211, 1213, . . . , 1219)even without the use of the power manager. The signal sensors (1205) tapinto these signals to look up estimated power requirements for thecomponents from lookup table (1209). A typical tapped signal can be usedto classify the state of the component into one of a plurality ofpossible states. When operating in some of the states, the componentconsume less than the maximum possible power. Thus, the signals can beused to look up more accurate power requirements according to theoperation condition of the components. Further, one of the sensors inthe signal sensors (1205) may be measuring the power levels of one orseveral different components.

When a component does not have a signal tapped to obtain a betterestimation, a conservative estimate based on the maximum possible powerused by the component is used. The power manager adds the powerrequirement for the components (e.g., 1211, 1213, . . . , 1219, 1221, .. . , 1229) to obtain an estimate of power that may be used in thesubsequent time interval.

In one embodiment, based on the power requirement for these componentsand the past power usage history, the power manager (1207) furtherdetermines a throttle setting for the main microprocessor(s) (1201) sothat the power usage within the next time period will be within thelimit of a power constraint even when the main microprocessor(s) arefully busy in the next time period.

In one embodiment, the power manager is partially or entirelyimplemented as a software module running on the main microprocessor(s)(1201). The lookup table (1209) can also be implemented using a softwaremodule using a random access memory of the computer system or using adedicated hardware module.

FIGS. 13 □16 illustrate methods of power management according toembodiments of the present invention.

In FIG. 13, operation 1301 determines actual power usage informationduring a first time period of operation of a data processing system. Theactual power usage information can be in the form of measurement ofpower, or current (e.g., at a known voltage), or power averaged in time,or current averaged in time, or measurements of other quantities thatare indicative of actual power usage. Operation 1303 determines aperformance level setting of a component of the data processing systemfor a second time period subsequent to the first time period using theactual power usage information. The system is set to the determinedperformance level setting to ensure that the power usage of the systemoperating at the determined performance level setting in the second timeperiod will not exceed any limit.

In FIG. 14, operation 1401 obtains N−1 samples of actual power usedduring a time period T1 of the operation of a data processing systemwhich has a set of throttled components and a set of non-throttledcomponents. In one embodiment, throttled components have differentadjustable performance level settings that have different power usagerequirements; and non-throttled components are not activelymanaged/controlled to trade performance level for power usage.

Operation 1403 estimates a power usage requirement of the non-throttledcomponents in a subsequent time period T2 of the operation of the dataprocessing system.

Operation 1405 sorts different combinations of throttle settingsaccording to a desired priority for processing in an order of decreasingpriority. In one embodiment, the priorities of the throttle settingsdepend on the current workload of the different throttled components;and the sorting is performed in real time. In one embodiment, thepriorities of the throttle settings are designed to be independent fromthe current workload of the different throttled components; and thesorting can be performed only once during the design or installationphase.

Operation 1407 processes one combination of throttle settings. Operation1409 computes a power usage indicator based on the N □□1 samples fortime period T1, the estimated power usage requirement of thenon-throttled components for time period T2, and the power usagerequirement of the throttled components at the combination of throttlesettings for time period T2. For example, the power usage indicator canbe an average power usage, an average battery discharge current, anaverage heat generation, etc.

If operation 1411 determines the computed power usage indicator is notallowable, operation 1413 processes the next combination; and operation1409 is repeated, until operation 1411 determines the computed powerusage indicator is allowable. In one embodiment, the settings aredefined (e.g., by design) so that at least one combination is alwaysallowable; thus, the loop of operations 1409, 1411 and 1413 eventuallyexits to operation 1415.

When the computed power usage indicator is allowable for a combinationof throttle settings, operation 1415 selects this combination for thetime period T2. Operation 1417 throttles the throttled componentsaccording to the selected combination of throttle settings for the timeperiod T2.

Operation 1419 obtains one or more samples of actual power used duringthe time period T2 while the throttled components are at the selectedcombination of throttle settings. Operation 1421 shifts time windowforward to determine throttle settings for the subsequent time period.Thus, operations 1403 through 1421 can be repeated for the subsequenttime period.

In FIG. 15, operation 1501 obtains one or more operating signals from afirst component of the data processing system. In one embodiment, theoperation signals are present for the normal operations of thecomponents regardless whether or not the components are under powerbudget control according to embodiments of the present invention. Suchan arrangement can minimize the impact of implementing methods ofembodiments of the present invention on the design of the non-throttledcomponents. Alternatively, the non-throttled components may bespecifically designed to provide signals to dynamically indicate theirpower usage requirements.

Operation 1503 determines an estimate of a power consumption requirementfor one or more components, including the first component, of the dataprocessing system for operating under a current condition.

In FIG. 16, operation 1601 obtains one or more operating signals fromeach of a first set of non-throttled components of a computer. Operation1603 determines the global state of each of the first set ofnon-throttled components. Operation 1605 looks up a power usagerequirement for each of the first set of non-throttled componentsaccording to the global state. Operation 1607 adds the power usagerequirements of the first set of non-throttled components with the powerusage requirement of the rest of non-throttled components to obtain thepower usage requirement for the non-throttled components. Operation 1609determines one or more throttle settings of a set of throttledcomponents of the computer based on the actual power usage in the pastand the power usage requirement for the non-throttled components.

Many of the methods of the present invention may be performed with adigital processing system, such as a conventional, general-purposecomputer system. Special purpose computers, which are designed orprogrammed to perform only one function, may also be used.

FIG. 17 shows one example of a typical computer system which may be usedwith the present invention. Note that while FIG. 17 illustrates variouscomponents of a computer system, it is not intended to represent anyparticular architecture or manner of interconnecting the components assuch details are not germane to the present invention. It will also beappreciated that network computers and other data processing systemswhich have fewer components or perhaps more components may also be usedwith the present invention. The computer system of FIG. 17 may, forexample, be an Apple Macintosh computer.

As shown in FIG. 17, the computer system 1701, which is a form of a dataprocessing system, includes a bus 1702 which is coupled to amicroprocessor 1703 and a ROM 1707 and volatile RAM 1705 and anon-volatile memory 1706. The microprocessor 1703, which may be, forexample, a G3, G4, or G5 microprocessor from Motorola, Inc. or IBM or aPentium microprocessor from Intel is coupled to cache memory 1704 asshown in the example of FIG. 17. The bus 1702 interconnects thesevarious components together and also interconnects these components1703, 1707, 1705, and 1706 to a display controller and display device1708 and to peripheral devices such as input/output (I/O) devices whichmay be mice, keyboards, modems, network interfaces, printers, scanners,video cameras and other devices which are well known in the art.Typically, the input/output devices 1710 are coupled to the systemthrough input/output controllers 1709. The volatile RAM 1705 istypically implemented as dynamic RAM (DRAM) which requires powercontinually in order to refresh or maintain the data in the memory. Thenon-volatile memory 1706 is typically a magnetic hard drive or amagnetic optical drive or an optical drive or a DVD RAM or other type ofmemory systems which maintain data even after power is removed from thesystem. Typically, the non-volatile memory will also be a random accessmemory although this is not required. While FIG. 17 shows that thenon-volatile memory is a local device coupled directly to the rest ofthe components in the data processing system, it will be appreciatedthat the present invention may utilize a non-volatile memory which isremote from the system, such as a network storage device which iscoupled to the data processing system through a network interface suchas a modem or Ethernet interface. The bus 1702 may include one or morebuses connected to each other through various bridges, controllersand/or adapters as is well known in the art. In one embodiment the I/Ocontroller 1709 includes a USB (Universal Serial Bus) adapter forcontrolling USB peripherals, and/or an IEEE-1394 bus adapter forcontrolling IEEE-1394 peripherals.

In one embodiment of the present invention, at least some of thecomponents can be actively throttled to trade performance for powerusage. For example, the microprocessor 1703 may have different corevoltage and frequency settings.

In one embodiment of the present invention, the system 1701 furtherincludes power usages sensor(s) 1711 that are coupled to the I/Ocontroller(s) 1709. One or more sensors may be used to determine thepower usage of the Central Processing Unit (CPU) (e.g., microprocessor1703) and/or the Graphical Processing Unit (GPU) (e.g., a processor ofthe display controller 1708). Further, one or more sensor may bedirectly coupled to the CPU and/or GPU. The power usage sensor(s) 1711may include one or more current sensors measuring the actual currentdrawn by the throttled components, and/or the actual current drawn bythe throttled components, and/or the actual current drawn by the system.In one embodiment, the power usage sensor(s) 1711 may include a crudepower usage sensor for a non-throttled component to determine the globalstate of the component, which can be used to dynamically estimate thepower usage requirement of the component.

In one embodiment of the present invention, the microprocessor 1703dynamically budgets power usage and determines throttle settingsaccording to instruction stored in cache 1704, ROM 1707, RAM 1705,and/or nonvolatile memory 1706. Alternatively, the system 1701 furtherincludes a microcontroller (not shown in FIG. 17) to dynamically budgetpower usage and determine throttle settings. In one embodiment, the dataprocessing system may include multiple central processing unit(CPU)/microproeessors.

It will be apparent from this description that aspects of the presentinvention may be embodied, at least in part, in software. That is, thetechniques may be carried out in a computer system or other dataprocessing system in response to its processor, such as a microprocessoror a microcontroller, executing sequences of instructions contained in amemory, such as ROM 1707, volatile RAM 1705, non-volatile memory 1706,cache 1704, or other storage devices, or a remote storage device. Invarious embodiments, hardwired circuitry may be used in combination withsoftware instructions to implement the present invention. Thus, thetechniques are not limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system. In addition, throughout thisdescription, various functions and operations are described as beingperformed by or caused by software code to simplify description.However, those skilled in the art will recognize what is meant by suchexpressions is that the functions result from execution of the code by aprocessor, such as the microprocessor 1703, or a microcontroller.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods of the present invention. This executable software anddata may be stored in various places including for example ROM 1707,volatile RAM 1705, non-volatile memory 1706 and/or cache 1704 as shownin FIG. 17. Portions of this software and/or data may be stored in anyone of these storage devices.

Thus, a machine readable medium includes any mechanism that provides(i.e., stores and/or transmits) information in a form accessible by amachine (e.g., a computer, network device, personal digital assistant,manufacturing tool, any device with a set of one or more processors,etc.). For example, a machine readable medium includesrecordable/non-recordable media (e.g., read only memory (ROM); randomaccess memory (RAM); magnetic disk storage media; optical storage media;flash memory devices; etc.), as well as electrical, optical, acousticalor other forms of propagated signals (e.g., carrier waves, infraredsignals, digital signals, etc.); etc.

The methods of the present invention can be implemented using dedicatedhardware (e.g., using Field Programmable Gate Arrays, or ApplicationSpecific Integrated Circuit) or shared circuitry (e.g., microprocessorsor microcontrollers under control of program instructions stored in amachine readable medium. The methods of the present invention can alsobe implemented as computer instructions for execution on a dataprocessing system, such as system 1701 of FIG. 17.

FIG. 18 is a flowchart of one embodiment of a method to dynamicallyredistribute power in a system. The method begins with operation 1801 ofidentifying a load profile of a system. The system has a plurality ofsubsystems. In one embodiment, the plurality of subsystems includesprocessors, e.g., a CPU, a GPU, a microcontroller, and the like. Thepower used by at least a subset of each of the subsystems is controlled,e.g., by a microcontroller, and a maximum power used by each of thesubsystems is determined by the dynamic power history of the wholesystem over an averaging period, as described above with respect toFIGS. 1-17. Such control of power allows higher performance operation inat least certain environments. That is, the subsystems may operate atbursts of substantially high power if there is a considerable low poweroperation, e.g., a idle time, during the averaging period, as describedabove with respect to FIGS. 1-17. In one embodiment, the power of thesubsystems may be controlled such a way that the maximum power used byeach of the subsystems may be increased or reduced in unison, e.g.,synchronously.

A load profile of the system is defined by workloads of each of thesubsystems in the system. A workload of a subsystem may be determinedusing various techniques. In one embodiment, a workload of a subsystemdetermines the amount of power used by the subsystem in the system. Inanother embodiment, the operating system may determine the workload ofthe subsystem out from historical scheduling data, or an application mayexplicitly inform the system about the workload. Various applicationsprovide various workloads to each of the subsystems. For example,program development tools and scientific applications present a highload to the CPU, but almost no load to the GPU that leads to anasymmetric load profile of the system (e.g. the CPU consumes a lot morepower than the GPU). Many professional applications present analternating high workload to the CPU and to the GPU that results in analternating asymmetric load profile of the system. Advanced userinterfaces or graphics editing application present a high load to theGPU and a modest load to the CPU that leads to another asymmetric loadprofile to the system. In one embodiment, the load profile may beidentified using workloads determined by measuring/sensing power (e.g.current drawn) by each subsystem or by measuring power for certainsubsystems and estimating or predicting power for other subsystems or byestimating power for all subsystems. In another embodiment, the loadprofile may be identified using workloads determined by the operatingsystem out from historical scheduling data. In yet another embodiment,to identify the load profile of the system, the information about theworkload of the subsystem provided by an application may be used.

After determining the load profile of the system, the method 1800continues with operation 1802, of redistributing the power of the systemamong the subsystems based on the load profile. In one embodiment, thepower is redistributed in an asymmetric fashion, tracking the workloadsof each of the subsystems. Such an asymmetric redistribution of thepower improves the user experience, because it allows a system that isincapable of running all of its subsystems at a full speed to appear tobe able to do so for many applications. In particular, detecting theasymmetric workloads of the subsystem, and redistributing the power inasymmetric fashion while tracking the workloads of the subsystems isimportant for small data processing systems such as portable computersor small desktop computers or handheld systems that may be incapable ofrunning all of their subsystems at full speed.

FIG. 19 is a flowchart 1900 of one embodiment of a method to dynamicallyredistribute power by tracking a load profile of a system. The methodbegins with operation 1901 by sensing an actual power used by each ofsubsystems in a system. In one embodiment, a maximum power of asubsystem is controlled, as described above with respect to FIGS. 1-17.In one embodiment, an actual power used by each of the subsystems ismeasured by one or more sensors (not shown) coupled to each of thesubsystems. One or more sensors may be connected to each of thesubsystems using, for example, a wire, or the sensors may be directlyattached to the subsystems. In one embodiment, the one or more sensorsused to measure actual power usage by each of the subsystems are thesensors as described above with respect to FIGS. 4, 5, 12, and 17.Alternatively, power consumed by one or more subsystems may beestimated. Next, in operation 1902, an utilization factor for each ofthe subsystems in the system is determined. The utilization factor maybe considered as a power efficiency metric for a subsystem in thesystem. In one embodiment, the utilization factor is a ratio of thepower used by the subsystem, e.g., the power measured by a sensor over atime interval divided by the power that is budgeted (“allocated”) to thesystem (or the subsystem itself) over the same time interval. In oneembodiment, the power for the system is allocated through the powerredistribution algorithm, using the dynamic power history of the wholesystem over an averaging period, as described above with respect toFIGS. 1-17. In one embodiment, the utilization factor for each of thesubsystems is a number between 0 and 1.0. In alternate embodiments,other numbers for the utilization ratio for each of the subsystems maybe used. A value of 1.0 may indicate a full utilization of allocatedsystem power by a subsystem, and a value oft) may indicate that thesubsystem is in a low power mode, e.g., is turned off, idle, or in asleeping mode.

Next, the method 1900 continues with operation 1903 which involvesdetermining a load profile of the system based on the utilizationfactors of each of the subsystems. In one embodiment, each of thesubsystems has a controlled operating power. In one embodiment, the loadprofile of the system is calculated using the utilization factors ofeach of the subsystems. For example, to calculate the load profile ofthe system having two subsystems, a utilization factor of one subsystemis subtracted from the utilization factor of the other subsystem. Forexample, in the system that contains two subsystems, if the utilizationfactor of a first subsystem is 0.25, and the utilization factor of asecond subsystem is 0.75, the load profile of the system is 0.5. Thatis, the load profile is shifted asymmetrically towards the secondsubsystem. In one embodiment, the load profile of the system, whichcontains two subsystems calculated using the utilization factors of eachof the subsystems that are numbers in the approximate range of 0.0 to1.0, is a number in the approximate range of 1.0 to −1.0. Further, avalue near 1.0 or −1.0 may indicate a substantially asymmetric loadprofile of the system. The substantially asymmetric load profile meansthat the power of the system could be shifted to operate one or more ofthe subsystems at high power, while the other one or more of thesubsystems operate at a low power, e.g., are idle. Further, for example,if the utilization factor of the first subsystem and the utilizationfactor of the second subsystem are approximately equal, the load profileis about 0.0. Numbers about 0.0 may indicate that the load profile isbalanced. That is, the power of the system could be evenly redistributedamong subsystems in the system. In another embodiment, the load profileis an array of numbers that includes the utilization factors of each ofthe subsystems. For example, for the system having two subsystems, theload profile of [1.0, 0.5] or [−0.5, −1.0] is an asymmetric loadprofile, and the load profile of [0.5, −0.5] is a balanced load profile.It will be appreciated that a variety of alternative ways to define andcalculate the load profile and/or the utilization factors may be used inalternative embodiments of the invention.

After determining the load profile of the system, the method 1900continues with operation 1904 which involves selecting a power weightingstrategy (which may be considered a power weighting arrangement amongthe subsystems) of the system based on the load profile. In oneembodiment, the power weighting strategy is selected based on the valueof the load profile. The power weighting strategy may be an asymmetricweighting, or a balanced weighting. For example, if the load profile isabout 1, −1, [1.0, 0.5], or [−0.5, −1.0], a substantially asymmetricpower weighting arrangement among the subsystems is selected. Forexample, if the load profile is about 0.0, [0.5, 0.5], or [0.5, −0.5], asubstantially balanced power weighting arrangement among the subsystemsis selected. In one embodiment, if all subsystems of the system areidle, the load profile is about 0.0, and the balanced strategy isselected. That is, the system is balanced at rest, and only movestowards an unbalanced strategy when it is actively unbalanced. Incertain embodiments, this allows the system to correct itself veryquickly by shifting from an asymmetric power strategy to a balancedpower strategy.

In one embodiment, a power weighting strategy is selected based on theload profile by selecting a power distribution table out of a pluralityof power distribution tables stored in a memory of the system.Generating the power distribution tables corresponding to various loadprofiles is described in further detail below with respect to FIGS. 22Ato 22C, and FIG. 23.

FIG. 20 is a flowchart 2000 of another embodiment of a method todynamically redistribute power while tracking a load profile of asystem. The method 2000 begins with operation 2001 of sensing (e.g.measuring) an actual power used by each of subsystems in a system, asdescribed above with respect to FIG. 19. Alternatively, power used bysome subsystems may be measured while power used by other subsystems maybe estimated or power used by all subsystems may be estimated. Themethod 2000 continues with operation 2002 which involves calculating foreach of the subsystems a ratio of the power used by a subsystem to thepower allocated to the system or that subsystem (the utilizationfactor). The utilization factor for each of the subsystems may becalculated as described above with respect to FIG. 19. Next, inoperation 2003, a current load profile of the system is determined usingratios of each of the subsystems, as described above with respect toFIG. 19. In operation 2004, a determination is made whether the currentload profile is different (or different enough) from a load profiledetermined in a time interval preceding the current time interval. Inone embodiment, the current load profile of the system is compared withthe previous load profile stored in a memory. If the current loadprofile is different from the previous load profile, a powerdistribution table corresponding to the current load profile is selectedin operation 2005. If the current load profile is not different (or notdifferent enough, for example, the difference is less than a thresholdvalue) from the previous load profile, the method continues withoperation 2001 of sensing the power used by each of the subsystems.

FIGS. 21A-21C illustrate one embodiment of power distribution tablesbuilt for a system that includes a CPU subsystem and a GPU subsystem.The power distribution tables depicted in FIGS. 21A-21C correspond todifferent load profiles, e.g., asymmetric and balanced load profiles. Asshown in FIGS. 21A-21C, the table 2100 has columns 2101, 2102, 2103, and2104, and rows 2105. Rows 2105 correspond to different settings of thesystem for a balanced load strategy. Settings of the system may be,e.g., different clock, and different core voltage settings, as describedabove with respect to FIGS. 1-17. Each setting is associated with anamount of power P allocated to the system. As shown in FIGS. 21A-21C,the amounts P1-P3 of power P allocated to the system at settings 1-3 isthe same for all tables 2100, 2110 and 2120. Column 2104 includesamounts P1-P3 of power P allocated for the system at various system'ssettings. In one embodiment, allocating the power P corresponding todifferent system's settings within a given table is performed usingmethods described above with respect to FIGS. 1-17. Columns 2102 and2103 include amounts of power that each of the subsystems, e.g., CPU andGPU, uses to operate at different system settings. FIG. 21A illustratesa power distribution table that corresponds to a balanced load profileK1 (e.g., 0.5, 0.5). As shown in FIG. 21A, each of CPU and GPU consumesabout a half of the power allocated to the system at each of thesettings 1-3. That is, for table 2100 the power of the system issubstantially evenly distributed (e.g. allocated or budgeted) among thesubsystems and corresponds to a balanced load profile K1 e.g., [0.5,0.5] of the system. Table 2100 may be selected to run applications thatrequire both CPU and GPU to be about equally busy. FIG. 21B illustratesa power distribution table that corresponds to a CPU-heavy load profileK2 e.g., [0.75, 0.25]. As shown in FIG. 21B, at each of settings 1 to 3,CPU consumes about 75%, while GPU consumes about 25% of the powerallocated for the system. That is, for table 2110, the power of thesystem is shifted towards CPU and corresponds to an asymmetric loadprofile K2 e.g., [0.75, 0.25], of the system. Table 2110 may be selectedwhen workload of the system is very CPU intensive, such that CPUconsumes a substantially bigger share of the total system's power, whilea graphics processor is hardly used at all. FIG. 21C illustrates a powerdistribution table that corresponds to a GPU-heavy load profile K3 e.g.,[0.25, 0.75]. As shown in FIG. 21C, at each of the settings 1 to 3, GPUconsumes about 75%, while CPU consumes about 25% of the power allocatedfor the system. That is, for table 2120, the power of the system isshifted towards GPU and corresponds to an asymmetric load profile K3e.g., [0.75, 0.25] of the system. Table 2110 may be selected whenworkload of the system is very graphics intensive, such that GPUconsumes a substantially bigger share of the total system's power, whilea CPU is hardly used at all. As shown in FIGS. 21A-21C, tables 2100,2110, and 2120 are built such a way that if a system moves from onesystem setting, e.g. from setting 1, to another system setting, e.g.,setting 2, the power needed to operate each of the subsystems increaseor decrease at the same time. That is, for each of tables 2100, 2110,and 2120, when performance of the system transitions between systempower settings 1-3 within the same power distribution table, the loadprofile of the system does not change. Selecting a table out of aplurality of power distribution tables associated with different loadprofiles while tracking the workloads of each of the subsystems usingone or more sensors provides an opportunity to select a proper table,e.g., table 2100, 2110, or 2120, based on the load profile of the systemat a current moment of time. That is, by just following a power usagepattern, the system's character can be dynamically changed with asubstantially high accuracy, so as to be a balanced system, a CPU-heavymachine, a GPU-heavy system, or any other subsystem-heavy system,without using complicated software. As a result, efficient, dynamicpower management for the system is provided, where portions of thehardware (processors, buses, memories, and other subsystems) may havetheir performance, and, as a side effect, their power, adjusted fairlytransparently to the software, based on the present workloads of thesubsystems. For example, the power of the system may be provided to amore busy GPU, while causing the CPU to slow down without affectingperformance of the CPU. That is, a GPU can work at a higher speed inexchange for slowing down the operations on the CPU without visiblyaffecting the user's perception of the performance of the CPU while theGPU's performance is visibly improved. In another embodiment, forcompute bound applications and memory bound applications, the power ofthe system may be dynamically redistributed between the CPU and amemory, while tracking actual workloads of each of the CPU and thememory. In yet another embodiment, the performance of a system, e.g., aportable computer, may alternate between GPU-heavy table 2120 andCPU-heavy table 2110 continuously (this kind of workload happensfrequently in the frame-by-frame processing of digital video). Thisdynamic shifting of allocated power to various subsystems, based onmonitoring a current load profile, is particularly useful for small,thin laptop computers which have small batteries and may not havecooling systems with large cooling capabilities, and this dynamicshifting is also particularly useful for small desktop computers (e.g.Mac Mini) which may not have cooling systems with large coolingcapabilities.

FIG. 22 illustrates one embodiment of one of power distribution tables2200 associated with a load profile Kn for a system, which includes aplurality of subsystems 1 to N. In one embodiment, subsystems 1 to Ninclude core logic functions, memory, CPU, disk drives, GPU, peripheraldevices, buses, and any other devices that have controlled power. Asshown in FIG. 22, the power distribution table includes columns2201-2205, and rows 2207. Rows 2207 correspond to different settings ofthe system, as described above with respect to FIGS. 21A-21C. Column2206 includes powers allocated for the system at different system'ssettings, as described above with respect to one of the tables in FIGS.21A-21C. In one embodiment, amounts P1-Pm of power P may be placed indescending, or ascending order across column 2206. Columns 2201-2204include amounts P1-Pn of power P that each of the subsystems 1-N isallocated to operate at different system settings. The system may haveany number of power distribution tables 2200 associated with differentload profiles of the system depending on the operating points that areneeded to be enabled in a system. In one embodiment, the plurality oftables 2200 have the same amounts P1-Pm of power P allocated for thesystem at settings 1 . . . M. Tables, such as table 2200, are created insuch a way that a sum of the powers needed to operate each of thesubsystems at each of settings 1-M (across each of rows 2207) does notexceed a corresponding amount of power P1-Pm allocated to the system ata corresponding setting 1-M. Tables, such as table 2200, may differ fromone another by a proportion of work that each subsystem 1-N is allowedto perform for a load profile Kn. After a power distribution table 2200is selected, the system's performance may move up and down across column2206. In one embodiment, if the performance of the system requires anincrease in total power, the performance moves up across the columne.g., from system's setting M to system's setting 1. In one embodiment,entries A1 . . . Am, B1 . . . Bm, and C1 . . . Cm into table 2200 may beprovided from the characteristics of the components from which each ofthe subsystems 1 . . . N is built. For example, a palette offrequencies, and/or core voltages may be used to produce power entriesfor CPU and GPU.

In one embodiment, the amounts of power in table 2200 may be representedin power units, e.g., watts, milliwatts, and the like, or in arbitraryunits. In another embodiment, instead of the amounts of power in powerunits, table 2200 may include various amounts of current needed tooperate the system and the needs of each of the subsystems at differentsystem settings represented in units of current, e.g., amperes,milliamperes, and the like. In alternate embodiments, table 2200 mayinclude various operating frequencies or voltages that correspond todifferent systems settings.

FIG. 23 is a flowchart of one embodiment of a method to dynamicallyredistribute power while tracking a load profile of a system whenanother subsystem is added to the system. The method begins withoperation 2301 of adding another subsystem to a plurality of subsystems.In one embodiment, another subsystem, e.g., a second CPU, or aperipheral device, is added to the system which already includes a firstCPU and a GPU. Adding another CPU or a peripheral device to theprocessing system is known to one of ordinary skill in the computer art.Next, in operation 2302 identifying another load profile of the systemthat includes an added subsystem is performed, as described above withrespect to FIGS. 19 and 20. In one embodiment, the another load profileis identified by determining the utilization factor of the addedsubsystem, as described above with respect to FIGS. 19 and 20. The loadprofile is then calculated using the utilization factors for each of thesubsystems including the added subsystem, as described above withrespect to FIGS. 19 and 20. Next, in operation 2303, the power of thesystem is redistributed between the subsystems based on the another loadprofile, as described above with respect to FIGS. 19 and 20. In oneembodiment, adding another subsystem may require more power to beallocated to the system. In such a case, the power of the system may beredistributed by selecting a power distribution table associated withanother load profile and with more total allocated power.

FIG. 24 illustrates one embodiment of a system to dynamicallyredistribute the power while tracking a load profile of a system asdescribed above with respect to FIGS. 18-23. As shown in FIG. 24 system2400 includes a subsystem 2401, e.g., a CPU, a subsystem 2402, e.g., aGPU that may be coupled with a display device, and one or moresubsystems 2409, e.g., one or more I/O controllers coupled to one ormore I/O devices, and a microcontroller 2407 coupled to a bus 2410.Further, system 2400 includes a volatile RAM 2404, a non-volatile memory2406, e.g., a hard drive, ROM 2403, and a cache memory 2405 coupled tosubsystem 2401 which is coupled to bus 2410. One or more sensors 2408,as described above with respect to FIGS. 4,5, 12, and 17 are coupled tosubsystems 2401, 2402, 2409, and to microcontroller 2407, as shown inFIG. 24. The sensors may be used to measure or estimate actual powerusage by one or more of the subsystems, and the sensors in turn providethe determined power usage values to the microcontroller which maycalculate the utilization factors and the corresponding load profile anduse the corresponding load profile to select a power distribution tablefrom the plurality of power distribution tables. Components of thesystem 2400, including processors, microcontrollers, buses, I/Ocontrollers, I/O devices, memories, sensors are described in detailabove with respect to FIGS. 1-17. In one embodiment, a plurality ofpower distribution tables corresponding to various load profiles asdescribed above with respect to FIGS. 21A-21C, and 22, may be generatedby subsystem 2401, and stored in any of memories 2406, 2404, and 2405 orwithin a memory in the microcontroller 2407. In one embodiment,microcontroller 2407 performs methods described above with respect toFIGS. 19-21 using power distribution tables generated when system 2400was designed. In another embodiment, subsystem 2401, rather thanmicrocontroller 2407, performs methods described above with respect toFIGS. 18-20 and in yet another embodiment, subsystem 2401 and themicrocontroller 2407 together perform the methods described above withrespect to FIGS. 19-20.

FIG. 25 is a flowchart of one embodiment of a method to adjust a targettemperature of a computer system or of a component in the computersystem. The method begins with operation 2501 of receiving a signalassociated with a temperature control of a component, e.g., a die. Inone embodiment, the component of the computer system is coupled to acooling system, e.g., a heat sink and the component includes anintegrated circuit which is a microprocessor. Generally, the heat sinkis an object used to take heat away from another object, such as amicroprocessor, to stabilize the temperature of the another object. Assuch, the heat sink can reduce the temperature of another object. FIG.26A illustrates one embodiment of a system having a component 2602,e.g., a die, coupled to a heat sink 2601. Heat sink 2601 takes the heataway from component 2602 through increased thermal mass relative, tomass of component 2602, and through heat dissipation by conduction,convection, and/or radiation. The heat sink may be made of a thermalconducting material, e.g., a metal, e.g., copper, aluminum, and the likemetals. To increase a thermal throughput, a thermal interface material(not shown), e.g., a thermally conductive grease or other material thatincludes, e.g., colloidal silver, may be placed between the componentand the heat sink.

As shown in FIG. 26A, heat sink 2601 includes a flat surface 2603 toensure a thermal contact with component 2602 to be cooled. As shown inFIG. 26A, heat sink includes an array of comb or tin like protrusions2604 to increase the surface contact with the air that may increase therate of the heat dissipation. The heat sink may be coupled to a fan (notshown) to increase the rate of airflow over the heat sink 2601 toincrease heat dissipation from the heat sink. Component 2602 may be amicroprocessor chip, a CPU, a GPU, a microcontroller chip, a memorychip, and/or any other power handling semiconductor device. In oneembodiment, component 2602 may be enclosed in a case. In one embodiment,component 2602 may be a microprocessor chip enclosed in a case, whereinthe microprocessor includes a logic circuitry (not shown) including oneor more monitors (not shown) that continuously monitor a temperature ofcomponent 2602.

For example, component 2602 may be a microprocessor enclosed in thecase, as produced by Intel Corporation, located in Santa Clara, Calif.If the temperature of component 2602 exceeds a component-specificthreshold, above which component 2602 may fail to operate, the logiccircuitry included in the microprocessor engages throttles that can slowdown the frequency of the microprocessor to avoid a failure. The logiccircuitry produces a signal associated with the temperature control ofcomponent 2602, which indicates that the temperature of component 2602reached the component-specific threshold, e.g. a die-specifiedthreshold. In one embodiment, the signal associated with the temperaturecontrol of component 2602 is the low-true signal that asserts thePROCHOT# (“PROCHOT_L”) pin of an Intel microprocessor. An assertion ofthe PROCHOT_L pin is an indirect indication that the temperature of heatsink 2601, is substantially high, such that the case, and component 2602enclosed in the case, have reached the maximum temperature (thecomponent-specified threshold) causing the assertion of PROCHOT_L pinsignal.

As shown in FIG. 26A, one or more temperature sensors 2605 monitor thetemperature of a computer system. In one embodiment, the one or moresensors 2605 are coupled to heat sink 2601 to monitor the temperature ofheat sink 2601 and these sensors are in turn coupled to a thermalcontroller which may be microcontroller which also receives thePROCHOT_L signal (or an indicator of the signal). The one or moretemperature sensors and the heat sink which is coupled to these sensorsand the thermal controller form a thermal control loop which adjustscooling operations (e.g. turning a fan or several fans on or off) inresponse to the sensed temperature of the heat sink. The thermal controlloop, through the control of the thermal controller, seeks to maintainthe measured temperature of the heat sink at or below a targettemperature, and the thermal controller adjusts the target temperatureup or down in response to assertions (and non-assertions) of the signalwhich is associated with thermal control of the component such as thePROCHOT_L signal which is on the PROCHOT_L pin). The temperature of heatsink is different from the temperature of component 2602, and/or thetemperature of the case that may enclose component 2602. The differencebetween the temperature of heat sink 2601 and component 2602, and/or thecase may be determined by a number of factors that include the nature ofheat sink 2601, the thermal interface material between component 2602and heat sink 2601, and a quality of component/heat sink assembly. Theone or more temperature sensors 2605 measure an integrated temperatureof heat sink to provide a temperature control of heat sink 2601. In oneembodiment, one or more temperature sensors 2605 are placed on a backside 2606 of heat sink 2601, e.g., a cold plate, which is opposite tosurface 2603, as shown in FIG. 26A. Positioning sensors 2605 on side2606 of heat sink 2601 opposite to side 2603 provides measuring anintegrated temperature of heat sink 2601 (where the temperature of theheat sink is in effect integrated or arranged over time by the physicalmass of the heat sink). That is, substantially all variations of thetemperature, e.g., related to component 2602, and/or other components(not shown) of the computer system are integrated into the measuredtemperature of heat sink 2601. Because of a substantially large thermalmass of heat sink 2601, the integrated temperature of heat sink 2601changes slowly, such that a temperature control loop of heat sink 2601does not observe fast temperature changes that are observed by anon-chip thermal sensor. Therefore, the temperature control loop of heatsink 2601 does not require a filter to filter out the fast temperaturechanges.

FIG. 26B illustrates a model of the thermal behavior of a heat sink. Asshown in FIG. 26B, the system includes a heat source 2611, e.g., a die,coupled to a heat storage 2612, e.g., a heat sink. Heat storage 2612 maybe considered the thermal inertia of the block of metal of the heatsink. Heat storage 2612 functions as a heat capacitor. As shown in FIG.26B, heat storage 2612 is coupled to a heat resistor 2613, e.g., a finof the heat sink. As shown in FIG. 26B, heat resistor 2613 is coupled toair 2614. The temperature measurement may be taken at position 2615between heat resistor 2613 and heat storage 2612, as shown in FIG. 26Bto filter out fast variations of the temperature while maintaining themeasurement of the integrated temperature. Position 2615 of one or moresensors to measure the temperature of the system 2610 is chosen tomaintain the balance between an integration over time and accuracy ofthe measurement to provide a stable and accurate temperature controlloop. Referring back to FIG. 26A, for example, if one or more sensors2605 are placed on surface 2603 of heat sink 2601 close to component2602, fast variations of the temperature of component 2602 may be sensedby sensors 2605 such that the integrated temperature of the heat sink isnot measured. In addition, fast variations of the temperature ofcomponent 2602 add noise to the measured temperature affecting theaccuracy. If one or more sensors 2605 are placed too far away fromcomponent 2602, e.g., on one of protrusions 2604 at the edge of heatsink 2601, the ambient temperature, e.g., air temperature, may impactmeasuring the integrated temperature of heat sink 3202. In oneembodiment, as shown in FIG. 26A, one or more sensors 2605 are placed ona back side of heat sink 2601, e.g., on a cold plate. The cold plate isa portion of heat sink 2601 where the heat energy is absorbed andtransferred from the heat sink 2601 to e.g., an outside ambient, and aheat removal apparatus, e.g., a fan. Positioning one or more sensors2605 on the cold plate provides measuring a substantially stabletemperature that reflects an amount of energy absorbed by heat sink 2601while minimizing the impact of temperature variations of component 2602.

The temperature control loop of heat sink 2601 controls, in at leastcertain embodiments, the temperature of heat sink 2601, such that thetemperature does not exceed a target temperature of the heat sink. Thetemperature control loop of the heat sink is described in further detailbelow with respect to FIGS. 27-29. Referring back to FIG. 25, the methodcontinues with operation 2502 which involves adjusting a targettemperature of the heat sink (or of the computer system) based on thesignal associated with the temperature control of the component, such asthe PROCHOT_L signal. In one embodiment, the target temperature of thecomputer system is determined by the target temperature of the coolingsystem, e.g., by the temperature of the heat sink. In one embodiment,the target temperature of the cooling system is the target temperatureof heat sink 2601. The target temperature of heat sink 2601 is adjusted,in at least certain embodiments, to operate component 2602 and the caseof component 2602 at a highest possible temperature with minimalcooling. The signal associated with temperature control loop ofcomponent 2602, e.g., an assertion of PROCHOT_L pin, provides theinformation to the temperature control loop of heat sink 2601 thatcomponent 2602 and the case of component 2602 have reached the highestpossible temperature without the need of knowing the exact value of suchhighest possible temperature.

FIG. 27 is a flowchart of one embodiment of a method of operating anadaptive cooling control system of a computer system. Method begins withoperation 2701 of operating an off-chip temperature control loop of thecomputer system. In one embodiment, the off-chip temperature controlloop is a heat sink temperature control loop. In one embodiment,operating the heat sink temperature control loop includes measuring atemperature of the heat sink using one or more sensors placed e.g., onthe heat sink, and controlling the temperature of the heat sink to stayjust below a target temperature of the heat sink, as described above byadjusting the performance of one or more cooling fans and/or othercooling devices. A thermal controller receives temperature measurementsfrom one or more sensors on the heat sink and decides whether to adjustthe performance of the cooling devices by comparing the temperaturemeasured on the heat sink to a target temperature for the heat sink. Ifthe measured temperature is less than the target temperature, thethermal controller can decrease the performance of one or more coolingdevices (e.g. fans, which generate noise, may be turned off) or canincrease the power of the microprocessor or other components, and if themeasured temperature is more than the target temperature then thethermal controller can increase the performance of one or more coolingdevices, e.g., turn on or increase the fan speed, or can decrease thepower of the microprocessor or other components, e.g., by decreasing theoperating voltage and/or operating frequency of the microprocessorand/or other components. Next, at operation 2702, a signal (e.g. thePROCHOT_L signal) associated with a temperature control loop of thecomponent is received, as described above. In one embodiment, thetemperature control loop of the component operates outside of andindependent of the cooling system temperature control loop. Thetemperature control loop of the component sets the target temperature ofthe temperature control loop of the cooling system, such as the targettemperature of the heat sink. At operation 2703, the target temperatureof the cooling system, e.g., the target temperature of the heat sink, isadjusted based on the signal. In one embodiment, adjusting the targettemperature of the cooling system is performed using a method describedbelow with respect to FIG. 28.

FIG. 28 is a flowchart of one embodiment of a method to adjust a targettemperature of a heat sink based on a component-specific signal. Themethod begins with operating a heat sink temperature control loop, asdescribed above. At operation 2802, a signal associated with atemperature control of a component, e.g., a PROCHOT_L pin, is asserted,as described above. Next, at operation 2803 an inquiry is made whetherthe signal has been asserted for more than a predetermined fraction oftime over a predetermined time interval. FIGS. 29A-29C illustratesignals associated with the temperature of the component according toone embodiment of the invention. As shown in FIGS. 29A-29C, each of thesignals has a duration Tsignal. The duration of the signal indicates forhow long the signal, e.g., PROCHOT_L pin is asserted. The duration ofthe signal Tsignal may be compared with a predetermined fraction of timeover a substantially long averaging time interval Tint, as shown inFIGS. 29A-29C. The signal may be asserted at any time over the timeinterval Tint and have any duration, as shown in FIGS. 29A-29C. In oneembodiment, the ratio Tsignal to Tint may be in the approximate range of0.001-0.99 depending on the design of the heat sink and the component.As shown in FIG. 29C, the signal having duration Tsignal may be asserteda number of times during the time interval Tint. Referring back to FIG.28, if the signal is asserted for more than a predetermined fraction oftime over a predetermined time interval, a target temperature of theheat sink is decreased at operation 2806. The target temperature may beadjusted by the factor that is a system dependent function of theaveraging time, the accuracy of the sensors, and other system dependentfeatures. In one embodiment, the amount of adjustment of the targettemperature is determined by the accuracy of temperature sensors. In oneembodiment, the target temperature (if the signal is asserted for morethan a predetermined fraction of time over a predetermined timeinterval), of the heat sink may be decreased by about 0.5% to about 30%.In one embodiment, if the signal is asserted for more than e.g., 0.5seconds to 3 seconds over e.g., 0.5 minutes to 20 minutes, a targettemperature of the heat sink is decreased by e.g., 0.5 degree to 3degree centigrade. Further, method 2800 goes back to operation 2801. Inone embodiment, a predetermined fraction of time for the signal to beasserted may be in the approximate range of 0.5 seconds to 3 seconds andthe predetermined time interval over which the predetermined fraction oftime is determined, may by in the approximate range of 0.5 minutes to 20minutes. In one embodiment, the temperature of the heat sink ismaintained just below the point at which the signal associated with thetemperature control of the component, e.g., a PROCHOT_L pin having aduration, e.g., in the approximate range of 0.5 seconds to 3 seconds isasserted frequently, e.g., not less than 2-5 times over an averagingtime interval, e.g., in the approximate range of 0.5 minutes to 20minutes. In another embodiment, the temperature of the heat sink ismaintained just below the point at which the signal associated with thetemperature control of the component, e.g., a PROCHOT_L pin, is assertedduring a substantially long time (e.g., has a substantially longduration, e.g., in the approximate range of 0.5 seconds to 3 secondsover an averaging time interval, e.g., in the approximate range of 0.5minutes to 20 minutes. In yet another embodiment, the temperature of theheat sink is maintained just below the point at which the signalassociated with the temperature control of the component, e.g., aPROCHOT_L pin having a duration, e.g., in the approximate range of 0.5seconds to 3 seconds is asserted frequently, e.g., not less than 2-3times and has a substantially long duration over an averaging timeinterval, e.g., in the approximate range of 0.5 minutes to 20 minutes.If the signal is not asserted for more than a predetermined fraction oftime, e.g., in the approximate range 0.5 seconds to 3 seconds, over thepredetermined time interval, e.g., 0.5 minutes to 20 minutes, the targettemperature may be optionally increased at operation 2805. In oneembodiment, the target temperature may be increased by about 0.5% toabout 30%. In one embodiment, if the signal is asserted for less thane.g., 0.5 seconds to 3 seconds over e.g., 0.5 minutes to 20 minutes, atarget temperature of the heat sink is increased by e.g., 0.5 degree to3 degree.

In another embodiment, if the signal is asserted for less than apredetermined fraction of time, e.g., in the approximate range 0.5seconds to 3 seconds, over the predetermined time interval, e.g., 0.5minutes to 20 minutes, the target temperature is not adjusted. Forexample, at certain workloads, or if a fan is temporarily obstructed,the component may generate the signal associated with the temperaturecontrol of the component e.g. PROCHOT_L no matter what temperature theheat sink is for small bursts, because it can not get the heat off thecomponent quickly enough. In such cases the target temperature may notbe adjusted. Further the method continues with operation 2801. That is,if the signal, e.g., the PROCHOT_L pin, asserts rarely, the controlsystem maintaining the heat sink temperature optionally increases thetarget temperature to operate the computer system with as little coolingas possible. As such, the temperature control system of the heat sinklearns the temperature that corresponds to the minimal amount ofcooling, independent of any small variations of the temperature in thecomponent and other components of the system. The temperature control ofthe computer system dynamically reacts to environmental changes. Bydynamically increasing or decreasing a target temperature of thecomputer system, the temperature control system dynamically adjust keyparameters of the computer system, e.g., an acoustics parameters, e.g. aspeed of a fan coupled to the heat sink, and/or a temperature of thecomputer system for a best case operation. That is, the computer systemcan operate with maximized efficiency at minimal cooling. In oneembodiment, operating with minimal cooling with maximized efficiencyincreases a gradient of the heat across the heat sink. The increasedgradient of the heat increases removal of the heat from the heat sinkwith less air flow. As a result, the cooling system may be operated moreefficiently acoustically. For example, if a fan is coupled to the heatsink, increased heat gradient across the heat sink may result inoperating the fan with a decreased speed. In one embodiment, thecomputer systems having the cooling system described with respect toFIGS. 25-29 may be, e.g., a small desktop computer, such as a Mac Mini,or a small laptop such as a small Power Book that are produced by AppleComputer, Inc., located in Cupertino, Calif.

FIG. 30 is a flowchart of another embodiment of a method of using acomponent-specific signal in a cooling system that includes a heat sink,as described above with respect to FIG. 26. Method 3000 determines orestimates a difference between the temperature of heat sink 2601 andcomponent 2602 when component 2602 is at a maximum temperature. Method3000 begins with operation 3001 of increasing a temperature of a heatsink until a signal associated with a temperature control of acomponent, e.g., a PROCHOT_L pin, is asserted. In one embodiment, thesystem is configured in such a way that thermal throttle that typicallycauses the component, e.g., a CPU, to slow down when PROCSHOT_L pin isasserted, is not activated, such that the component, e.g., a CPU,continues to operate at the same frequency as before the signal isasserted. In one embodiment, a maximum temperature of the component atwhich the signal associated with the temperature control of thecomponent is asserted, is measured. In one embodiment, the temperatureof the component may be measured using one or more sensors positioned onthe component. Method continues with operation 3002 of measuring thetemperature of the heat sink after the signal, e.g., the PROCHOT_L pin,is asserted. In one embodiment, the temperature of the heat sink may bemeasured using one or more temperature sensors coupled to the heat sink,as described above with respect to FIG. 26. Next, at operation 3003, adifference (delta) between a measured temperature of the heat sink andthe maximum temperature of the component is computed. The measuredtemperature of the heat sink may be subtracted from the maximumcomponent temperature. The difference between the temperature of theheat sink and the maximum component temperature provides a baseline foran efficient operational point of a computer system. Method continueswith operation 3004 of setting a baseline target temperature of the heatsink based on the difference. In one embodiment, the toleranceparameters for the heat sink/fan assembly may be set based on thecomputed difference between the temperature of the heat sink and themaximum component temperature. In one embodiment, a compensation for anambient temperature may be provided, because the thermal resistance of acooling system may not be linear with ambient temperature. For example,a plurality of measurements at a plurality of temperatures may beperformed to compensate for the ambient temperature.

FIG. 31 is a flowchart of one embodiment of a method to operate acooling system that includes a heat sink. Method begins with operation3101 of monitoring a temperature of a heat sink using one or moresensors, as described above with respect to FIG. 26. A component iscoupled to the heat sink, as described above with respect to FIG. 26. Atoperation 3102, a signal associated with a temperature control of thecomponent, e.g., a PROCHOT_L pin, is asserted, as described above withrespect to FIGS. 25, 27, and 28. Next, at operation 3103 a targettemperature of the heat sink is adjusted based on the asserted signal,as described above with respect to FIGS. 25, 27, and 28. Next, atoperation 3104 an operation of the component, a cooling unit, e.g., afan coupled to the component, or both, is adjusted based on arelationship between the monitored temperature of the heat sink and anadjusted target temperature of the heat sink. In one embodiment,adjusting the operation of the component includes changing an operatingfrequency of the component, an operating voltage of the component, orboth, and adjusting the cooling unit is performed by changing a speed ofa fan.

FIG. 32 illustrates one embodiment of a computer system 3200 having anadaptive cooling arrangement, as described above with respect to FIGS.25-31. As shown in FIG. 32 system 3200 includes a component 3201, e.g.,a CPU, a microprocessor, a GPU, a microcontroller, or any combinationthereof. As shown in FIG. 32, component 3201 is coupled to a coolingsystem 3210. As shown in FIG. 32, cooling system 3210 includes a heatsink 3202 coupled to a fan 3211, one or more sensors 3203 coupled toheat sink 3202 to measure and monitor temperature of heat sink 3202, anda power manager 3204, e.g., a microprocessor, to perform methodsdescribed above with respect to FIGS. 25-31. The power manager 3204 mayalso be referred to as thermal controller. A position of the one or moresensors 3203 in cooling system 3210 may be chosen to maintain the bestcompromise between measuring a stable and accurate temperature controlloop, as described above with respect to FIGS. 26A and 26B. In oneembodiment, one or more sensors 3203 are placed on a back side of heatsink 3202, e.g., on a cold plate. As shown in FIG. 32, component 3201,power manager 3204, e.g., a microcontroller, a subsystem 3205 thatincludes e.g., one or more I/O controllers coupled to one or more I/Odevices, are coupled through a bus 3209. Bus 3209 may include one ormore buses connected to each other through various bridges, controllersand/or adapters as is well known to one of ordinary skill in the art ofcomputer systems. As shown in FIG. 32, a volatile RAM 3207, anon-volatile memory 3208, e.g., a hard drive, and ROM 3206, are coupledto power manager 3204, component 3201 and subsystem 3205 through bus3209. In one embodiment, power manager 3204 receives a signal associatedwith a temperature control of component 3201 and adjusts a targettemperature of heat sink 3202 based on the received signal, as describedwith respect to FIGS. 25-31. In one embodiment, power manager 3204operates a temperature control loop of heat sink 3202. In oneembodiment, power manager 3204 increases the temperature of heat sink3202 to receive the signal associated with the temperature control ofcomponent 3201, measures the temperature of heat sink 3202, anddetermines a difference between a measured temperature of heat sink 3202and a maximum temperature of component 3201, as described above withrespect to FIG. 30. In one embodiment, power manager 3204 sets abaseline target temperature of heat sink 3202 based on the determineddifference. In another embodiment, power manager 3204 sets toleranceparameters and/or validates the tolerance parameters of the coolingsystem based on the determined difference. The power manager 3204 mayalso perform one or more of the methods described in connection withFIGS. 1-24.

FIG. 33 is a flowchart of one embodiment of a method to manage the powerof a computer system that leverages intermediate power points. Thecomputer system includes one or more components (“subsystems”). Thesubsystem may be a microprocessor, a microcontroller, a memory, a CPU, aGPU, or any combination thereof. The method begins with operation 3301of operating a subsystem at one or more performance points. Aperformance point may be, e.g., an operational frequency that may definean operational speed of the subsystem, a temperature, or a combinationthereof. For example, a processor may be operated at a set offrequencies, e.g., at 600 MHz, at 1 GHz, and at 2 GHz at a specifiedtemperature, e.g., a room temperature. The subsystem is operated atwell-known conditions at the performance point. For example, thesubsystem may be operated to consume the maximum power at theperformance point. In one embodiment, the well-known conditions arethose that are substantially close to the thermal design point (“TDP”)for a worst-case part of the subsystem. For example, the subsystem,e.g., a processor, may be operated with appropriately chosen software,e.g., a known real world application, or a diagnostic software built fortesting the processor, e.g., a power grading software. The methodcontinues with operation 3302 of measuring the actual power consumed bythe subsystem at each of the one or more performance points. The powermay be measured using a precision power measuring circuitry, e.g., oneor more sensors, described above with respect to FIGS. 12, 24, and 32.The power measuring circuitry may be built into the subsystem. In oneembodiment, the power may be measured at a performance point, which issubstantially close to TDP point for the processor, at a temperature atwhich the processor is operated. The method continues with operation3303 of determining an operational power of the subsystem based on themeasured power of the subsystem at the performance point. For example, ameasured actual power consumed by the subsystem, e.g., a processor, isused as an intermediate operational power allocated to the subsystem ata predetermined frequency. That is, the intermediate operational powerpoints are determined on a “per-subsystem” basis and may includeoperational power margins for the subsystem.

FIG. 38 illustrates one embodiment of a data processing system thatleverages intermediate operational power points for a subsystem in avaluable way. As shown in FIG. 38, a published specification power 3802for a subsystem, e.g., a CPU, is 80 W. Such a published specificationpower is a worst-case power value that is valid for a plurality ofsubsystems. For example, the published specification power 3802 may be aworst-case power value determined from a statistical power distributioncurve 3801 of a vast number of sample processors at a maximum frequencyof 3.0 GHz. As shown in FIG. 38, an intermediate operational power point3803 determined on a “per-subsystem” basis for the CPU at a frequency of2.5 GHz is 40 W. The intermediate operational power point 3803 is anactual measured power consumed by the subsystem, e.g., the CPU, at aperformance point, e.g., at a frequency 2.5 GHz. For example, at theperformance point of 1.0 GHz the intermediate operational power point3803 for the processor may be 20 W. That is, the intermediateoperational power point 3803 has an operational margin that issubstantially smaller than published specification power 3802, which maybe considered an established, predetermined value that has a worst-casepower margin defined from the statistical power distribution curve. Whenthe data processing system operates the CPU at these intermediate powerpoints the additional power (40 W or 60 W) may be used by the dataprocessing system to operate, for example, another subsystem, e.g., aGPU at various performance points. In other words, at a givenintermediate (or other) operational point (such as a given operatingfrequency at a given temperature), the system or subsystem may normallyconsume only a portion (e.g. 70%) of an amount of power which has beenreserved for it under a conservative, worst-case design; this margin isoften, in fact, unnecessary and can be used by the system or subsystem.In effect, the difference between worst-case and actual power can beallocated to other subsystems. As a further example, the power values inthe power distribution tables of FIGS. 21A-21C may include some or allor this difference for each subsystem which is controlled through theuse of these power distribution tables. As such, the performanceefficiency of the computer system may be substantially increased. In oneembodiment, the measured actual maximum power consumed by the subsystemis provided to a power look-up table, as described with respect to FIGS.6-7, and 21-22. As such, the power look up table is built on the fly, ona “per-subsystem” basis. In one embodiment, the power look up tables maybe built that include the measured actual maximum power consumed by eachof the subsystems at a set of performance points to allocate the powerfor the subsystem at different computer system settings. In oneembodiment, the measured actual maximum power values consumed by thesubsystem at a set of performance points are entered into the powerlook-up table as the power values that are allocated to the subsystem atvarious settings.

FIG. 34 is a flowchart of one embodiment of a method of providing anintermediate processor power margin for a subsystem. The method beginswith operation 3401 of operating a subsystem at well-known conditions ata performance point, as described above. The method continues withoperation 3402 of measuring the power consumed by the subsystem at theperformance point, as described above with respect to FIG. 33. Next,determining an operational power of the subsystem using the measuredpower is performed at operation 3403. In one embodiment, the operationalpower may include operational power margins. Further, at operation 3404the operational power of the subsystem may be optionally adjusted basedon another data associated with the subsystem. The another dataassociated with the subsystem may be a data provided by a feedbackcircuitry coupled to the subsystem. The feedback circuitry may providedata associated with the temperature the subsystem. For example, thedata associated with the subsystem may be a signal associated with atemperature of a die of the subsystem, e.g., an assertion of PROCHOT_Lpin, as described above. In one embodiment, the operational power may beadjusted to add extra power. The extra power may be added to include adesign margin for measuring error, measuring accuracy, and/or measuringresolution of the power measuring circuitry. For example, if the powermeasuring circuitry has a measuring error, e.g., in the approximaterange of 1 to 5%, the measured operational power may be adjusted toinclude the measuring error. The extra power may be added to include adesign margin for a temperature and a margin to a future higher powercode. The adjusted power may be used to provide entries to power look-uptables of the computer system, e.g., as described above with respect toFIGS. 6, 7, 21, and 22. The adjusted operational power values may beused to operate the subsystem. Next, at operation 3405 the operationalpower for the subsystem is provided to a power lookup table of acomputer system. In one embodiment, the power look up table of thecomputer system may be a power look up table as described above withrespect to FIGS. 6, 7, 21, and 22. Next, at operation 3406 determinationis made whether to operate the system at a next performance point, e.g.,at another frequency, another temperature, or both. Operations 3401-3405are repeated, if the subsystem is needed to operate at the nextperformance point. If the subsystem is not needed to operate at the nextperformance point, method 3400 continues with operation 3407 of usingthe operational power points to operate the subsystem or to store valuesin power distribution tables for use in machines to be manufactured.

FIG. 35 is a flowchart of another embodiment of a method of usingintermediate operational power points to distribute power in a computersystem. Method 3500 begins with operation 3501 of operating one or moresubsystems of a computer system at well-known conditions at one or moreperformance points. In operation 3502 the power consumed by each of theone or more subsystems at each of the one or more performance points ismeasured. In one embodiment, measuring the power consumed by each of thesubsystems is performed in parallel. For example, when the subsystemsare operated at the same time, the measuring may be performed inparallel using one or more sensors coupled to the one or moresubsystems. In another embodiment, measuring the power consumed by eachof the subsystems is performed in series. For example, when onesubsystem at a time is operated at the well-known conditions, measuringmay be performed in series using one or more sensors coupled to thesubsystems. Next, determining operational powers for each of the one ormore subsystems is performed in operation 3503. Next, distributing thepower among the subsystems of the computer system is performed based onthe operational powers of each of the one or more subsystems inoperation 3504. Accurate knowledge of the intermediate powers at each ofthe performance points allow for designs that carefully adjust theperformance of each of the subsystems in the computer system to allowmaximum possible performance under a current set of constraints, e.g.,thermal or electrical power constraints, for the computer system. Themeasuring of intermediate operational powers and the incorporating ofthe results of these measurements provide a platform specific basis forpower distribution and balancing in one or more subsystems of thecomputer system. In one embodiment, the platform specific intermediateoperational powers may be determined as a part of the factory testprocess and written into the system management controller (“SMC”) of thecomputer system for use in the power balancing algorithm. In anotherembodiment, the platform specific intermediate operational powers may bedetermined periodically during a life time of a computer system, andstored in SMC. Next, at operation 3504, distributing the power among thesubsystems of a computer system is performed based on the operationalpowers allocated to each of the subsystems to balance the power in thecomputer system. In one embodiment, power look up tables may be builtthat include the measured actual power consumed by each of thesubsystems at maximum-power conditions and at a set of performancepoints to distribute and balance the power among the subsystems atdifferent computer system settings. In one embodiment, the measuredactual power values consumed by each of the subsystems at maximum-powerconditions and at a set of performance points are entered into the powerlook-up table as the power values allocated to the each of thesubsystems at various settings.

FIG. 36 is a flowchart of one embodiment of a method of determiningintermediate operational powers of one or more subsystems of a computersystem. This method begins with operation 3601 which involves operatinga subsystem of a computer system at well-known conditions at one or moreperformance points. In operation 3602 the power consumed by thesubsystem at each of the one or more performance points is measured todetermine operational powers for the subsystem at each of the one ormore performance points. Next, determination is made at operation 3603whether operational power is to be determined for another subsystem. Ifthe operational power is to be determined for another subsystem, method3600 returns to performing operations 3601-3602 for another subsystem.If the operational power for another subsystem does not need to bedetermined, method 3600 continues with operation 3604 of distributingthe power in the computer system using the operational powers determinedbased on the measured power of the one or more subsystem.

FIG. 37 illustrates one embodiment of a system that leveragesintermediate operational power margins and distributes the power asdescribed above with respect to FIGS. 33-36. As shown in FIG. 37 system3700 includes a subsystem A 3701, e.g., a CPU, a subsystem B 3702. e.g.,a GPU that may be coupled with a display device, subsystem C 3704, e.g.,a memory, subsystem D 3705, e.g., a microprocessor, and one or moresubsystems N 3703, e.g., one or more I/O controllers coupled to one ormore I/O devices, a power manager 3708, e.g., a microcontroller, asystem management controller (“SMC”), coupled to a interconnect 3706,e.g., a bus. Subsystem C 3704 may be a volatile RAM, a non-volatilememory, e.g., a hard drive, and/or a ROM. One or more measuring devices3707, e.g., one or more sensors as described above with respect to FIGS.4, 5, 12, 17, 24, and 32 are coupled to subsystems 3701-3705, and topower manager 3708, as shown in FIG. 37. A power look-up table 3709 thatmay include a power distribution table, as described above with respectto FIGS. 7,21, and 22, is coupled to power manager 3708, as shown inFIG. 37. Components of the system 3700, including processors,microcontrollers, buses, I/O controllers, I/O devices, memories, sensorsare described in detail above with respect to FIGS. 1-17. In oneembodiment, one or more power lookup tables corresponding to variousperformance settings of the computer system as described above withrespect to FIGS. 1-36, may be generated by subsystem 3701 (or generatedby test equipment in the design and/or manufacturing process), andstored in memory 3704, and/or in a memory located in power manager 3708.In one embodiment, power manager 3708 performs methods described abovewith respect to FIGS. 33-36. In another embodiment, subsystem 3701performs methods described above with respect to FIGS. 33-36.

In the foregoing specification, the invention has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope of the invention as set forth in thefollowing claims. The specification and drawings are, accordingly, to beregarded in an illustrative sense rather than a restrictive sense.

What is claimed is:
 1. A data processing system, comprising: a plurality of components including a set of non-throttled components and a set of throttled components, wherein each throttled component is capable of being actively controlled to one or more corresponding throttle settings, and wherein each throttle setting includes a different combination of one or more operating settings for the corresponding throttled component; a plurality of sensors configured to determine information on power usage of the plurality of components during a first time period; and a processor configured to: estimate power usage of the set of non-throttled components during a second time period, and determine, according to a target goal level, one or more global throttle settings, wherein each global throttle setting corresponds to a set of throttle settings, and wherein the set of throttle settings includes a different combination of throttle settings for the throttled components such that power usage of the plurality of components during the second time period satisfies a system constraint.
 2. The data processing system of claim 1, wherein one or more non-throttled components are configured to provide respective state signals corresponding to respective global states, and wherein the processor is configured to estimate power usage of the one or more non-throttled components during the second time period based on the respective state signals.
 3. The data processing system of claim 1, wherein the target goal level is a function of a current state of the data processing system, and wherein the function is based at least in part on respective current workloads of one or more throttled components.
 4. The data processing system of claim 1, wherein the target goal level is based on a combination of required performance levels, and wherein the combination of required performance levels includes at least one of a required computing power level or a required energy consumption level.
 5. The data processing system of claim 1, wherein the processor is further configured to: sort the one or more global throttle settings in an order of priority according to the target goal level, and select a highest priority global throttle setting such that power usage of the plurality of components during the second time period satisfies the system constraint.
 6. The data processing system of claim 2, wherein the processor is configured to estimate, when a non-throttled component does not provide a respective state signal, that the power usage of the non-throttled component during the second time period is a maximum power usage for the non-throttled component.
 7. The data processing system of claim 3, wherein the processor is configured to estimate the respective current workloads of the one or more throttled components based on the information on power usage of the plurality of components during the first time period.
 8. The data processing system of claim 5, wherein the system constraint includes a limit on at least one of power usage, battery discharge current, thermal/heat dissipation, or heat generation.
 9. A non-transitory machine readable medium storing instructions which, when executed by a data processing system, cause the data processing system to perform a method to manage power usage in the data processing system, the method comprising: determining information on power usage of a plurality of components during a first time period, the plurality of components including a set of non-throttled components and a set of throttled components, wherein each throttled component is capable of being actively controlled to one or more corresponding throttle settings, and wherein each throttle setting includes a different combination of one or more operating settings for the corresponding throttled component; estimating power usage of the set of non-throttled components during a second time period; and determining, according to a target goal level, one or more global throttle settings, wherein each global throttle setting corresponds to a set of throttle settings, and wherein the set of throttle settings includes a different combination of throttle settings for the throttled components such that power usage of the plurality of components during the second time period satisfies a system constraint.
 10. The non-transitory machine readable medium of claim 9, the method further comprising: providing, by one or more non-throttled components, respective state signals corresponding to respective global states; and wherein estimating power usage of the one or more non-throttled components during the second time period is based on the respective state signals.
 11. The non-transitory machine readable medium of claim 9, wherein the target goal level is a function of a current state of the data processing system, and wherein the function is based at least in part on respective current workloads of one or more throttled components.
 12. The non-transitory machine readable medium of claim 9, wherein the target goal level is based on a combination of required performance levels, and wherein the combination of required performance levels includes at least one of a required computing power level or a required energy consumption level.
 13. The non-transitory machine readable medium of claim 9, the method further comprising: sorting the one or more global throttle settings in an order of priority according to the target goal level; and selecting a highest priority global throttle setting such that power usage of the plurality of components during the second time period satisfies the system constraint.
 14. The non-transitory machine readable medium of claim 10, the method further comprising: estimating, when a non-throttled component does not provide a respective state signal, that the power usage of the non-throttled component during the second time period is a maximum power usage for the non-throttled component.
 15. The non-transitory machine readable medium of claim 11, the method further comprising: estimating the respective current workloads of the one or more throttled components based on the information on power usage of the plurality of components during the first time period.
 16. The non-transitory machine readable medium of claim 13, wherein the system constraint includes a limit on at least one of power usage, battery discharge current, thermal/heat dissipation, or heat generation.
 17. A method comprising: determining information on power usage of a plurality of components during a first time period, the plurality of components including a set of non-throttled components and a set of throttled components, wherein each throttled component is capable of being actively controlled to one or more corresponding throttle settings, and wherein each throttle setting includes a different combination of one or more operating settings for the corresponding throttled component; estimating power usage of the set of non-throttled components during a second time period; and determining, according to a target goal level, one or more global throttle settings, wherein each global throttle setting corresponds to a set of throttle settings, and wherein the set of throttle settings includes a different combination of throttle settings for the throttled components such that power usage of the plurality of components during the second time period satisfies a system constraint.
 18. The method of claim 17 further comprising: providing, by one or more non-throttled components, respective state signals corresponding to respective global states; and wherein estimating power usage of the one or more non-throttled components during the second time period is based on the respective state signals.
 19. The method of claim 17, wherein the target goal level is a function of a current state of the data processing system, and wherein the function is based at least in part on respective current workloads of one or more throttled components.
 20. The method of claim 17, wherein the target goal level is based on a combination of required performance levels, and wherein the combination of required performance levels includes at least one of a required computing power level or a required energy consumption level.
 21. The method of claim 17, further comprising: sorting the one or more global throttle settings in an order of priority according to the target goal level; and selecting a highest priority global throttle setting such that power usage of the plurality of components during the second time period satisfies the system constraint.
 22. The method of claim 18 further comprising: estimating, when a non-throttled component does not provide a respective state signal, that the power usage of the non-throttled component during the second time period is a maximum power usage for the non-throttled component.
 23. The method of claim 19, further comprising: estimating the respective current workloads of the one or more throttled components based on the information on power usage of the plurality of components during the first time period.
 24. The method of claim 21, wherein the system constraint includes a limit on at least one of power usage, battery discharge current, thermal/heat dissipation, or heat generation. 